README_research_papers.md

Collection of recent research papers on Deep Learning, Machine Learning application

Research Papers related with Pytorch, Tensorflow implementations.

Vision

1. Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization Junjie Yan, Ruosi Wan, Xiangyu Zhang, Wei Zhang, Yichen Wei, Jian Sun Paper code

2. Fast Neural Network Adaptation via Parameter Remapping and Architecture Search Jiemin Fang*, Yuzhu Sun*, Kangjian Peng*, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang papepr code

3. Certified Defenses for Adversarial Patches Ping-yeh Chiang*, Renkun Ni*, Ahmed Abdelkader, Chen Zhu, Christoph Studor, Tom Goldstein paper code

4. Adaptively Connected Neural Networks. Guangrun Wang, Keze Wang, Liang Lin paper code

5. Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber paper code

6. EDVR: Video Restoration With Enhanced Deformable Convolutional Networks. Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, Chen Change Loy 2019 (modified: 03 Mar 2020) paper code

7. LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts. Shuming Ma, Lei Cui, Damai Dai, Furu Wei, Xu Sun 2019 (modified: 03 Mar 2020) paper code

8. A Hybrid Method for Tracking of Objects by UAVs. Hasan Saribas, Bedirhan Uzun, Burak Benligiray, Onur Eker, Hakan Cevikalp 2019 (modified: 03 Mar 2020) paper code

9. DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection. Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang 2019 (modified: 03 Mar 2020) paper code

10. Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-To-Image Translation. Matteo Tomei, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara 2019 (modified: 03 Mar 2020) paper code

11. ELASTIC: Improving CNNs With Dynamic Scaling Policies. Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, Alan L. Yuille, Mohammad Rastegari 2019 (modified: 03 Mar 2020) paper code

12. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification. Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun 2019 (modified: 03 Mar 2020) paper code

13. Recursive Visual Attention in Visual Dialog. Yulei Niu, Hanwang Zhang, Manli Zhang, Jianhong Zhang, Zhiwu Lu, Ji-Rong Wen 2019 (modified: 03 Mar 2020) paper code

14. Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks. He Zhang, Benjamin S. Riggan, Shuowen Hu, Nathaniel J. Short, Vishal M. Patel 2019 (modified: 03 Mar 2020) paper code

15. MnasNet: Platform-Aware Neural Architecture Search for Mobile. Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le 2019 (modified: 03 Mar 2020) paper code

16. Auto-Encoding Scene Graphs for Image Captioning. Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai 2019 (modified: 03 Mar 2020) paper code

17. Visualizing the Decision-making Process in Deep Neural Decision Forest. Shichao Li, Kwang-Ting Cheng 2019 (modified: 03 Mar 2020) paper sode

18. Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer. Shaohui Lin, Rongrong Ji, Chao Chen, Dacheng Tao, Jiebo Luo 2019 (modified: 03 Mar 2020) paper code

19. PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet. Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, Simon Lucey 2019 (modified: 03 Mar 2020) paper code

20. Learning to Parse Wireframes in Images of Man-Made Environments Kun Huang, Yifan Wang, Zihan Zhou, Tianjiao Ding, Shenghua Gao, Yi Ma 2018 (modified: 25 Feb 2020) paper code

21. Deep Model Transferability from Attribution Maps Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song 25 Feb 2020 paper code

22. [RE] Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks Xueqing Zhao, Jinsong Zhang, Hao Sun 29 Dec 2019 (modified: 29 Dec 2019) paper code)

23. Direction Concentration Learning: Enhancing Congruency in Machine Learning
    Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao 12 Feb 2020paper code

24. Deformable Convolutional Networks Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei 2017 (modified: 12 Feb 2020) paper code

25. Improving Visual Relation Detection using Depth Maps Sahand Sharifzadeh, Sina Moayed Baharlou, Max Berrendorf, Rajat Koner, Volker Tresp 26 Sep 2019 (modified: 24 Dec 2019) paper code

Reinforcement learning

1. Deep Reinforcement Learning for Organ Localization in CT Anonymous 25 Jan 2020 (modified: 11 Feb 2020) paper code

2. Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning Prithviraj Ammanabrolu, Mark Riedl 2019 (modified: 03 Mar 2020) paper code

3. EMI: Exploration with Mutual Information Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song 2019 (modified: 03 Mar 2020) paper supporting-document code

4. Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells. Vladimir Nekrasov, Hao Chen, Chunhua Shen, Ian D. Reid 2019 (modified: 03 Mar 2020) paper code

5. DAC Replication Report Yan Huang, Cancan Huang, Zhe Hu, Zezhi Wang, Ziyao Huang, Yichen Chai 02 Dec 2019 (modified: 29 Dec 2019) paper code

6. Reproducing “Towards Interpretable ReinforcementLearning Using Attention Augmented Agents” Charles Lovering, Sam Lobel, Denizalp Goktas, Kweku Kwegyir-Aggrey, Albert Webson 02 Dec 2019 (modified: 29 Dec 2019) paper code

7. DPO Reproducibility Challenge Report Jiuyang Bai, Gregory Cho, Linlin Liu, Xingchi (Miles) Yan, Liu Yang 02 Dec 2019 (modified: 29 Dec 2019) paper code

8. [Re] A Family of Robust Stochastic Operators for Reinforcement Learning Haoze Zhang, Lu Shao, Joseph Kijewski, Ishaan Shah, Nishant Kumar, Daniel Glickman 02 Dec 2019 (modified: 29 Dec 2019) paper code

9. [Replication] A Meta-MDP Approach to Exploration for LifelongReinforcement Learning David Cabatingan, Kendrick Cole, Petar Peshev, Natalie Delworth 02 Dec 2019 (modified: 29 Dec 2019) paper code

10. DPO Reproducibility Challenge Report Jiuyang Bai, Gregory Cho, Linlin Liu, Xingchi (Miles) Yan, Liu Yang 02 Dec 2019 (modified: 29 Dec 2019) paper code

11. Reproducibility Challenge: Meta-LearningRepresentations for Continual Learning Mihaela Georgieva Stoycheva, Sergio Liberman Bronfman, Konstantinos Saitas Zarkias 02 Dec 2019 (modified: 29 Dec 2019) paper code

12. Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning Yufei Wang*, Ziju Shen*, Zichao Long, Bin Dong 26 Sep 2019 (modified: 24 Dec 2019) paper code

Tabular

1. Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz 26 Sep 2019 (modified: 11 Mar 2020) paper code

2. Optimistic Exploration even with a Pessimistic Initialisation Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson 26 Sep 2019 (modified: 11 Mar 2020) paper code

3. Exploration in Reinforcement Learning with Deep Covering Options Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris 26 Sep 2019 (modified: 11 Mar 2020) paper code

4. Optimistic Exploration even with a Pessimistic Initialisation Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson 26 Sep 2019 (modified: 11 Mar 2020) paper code

5. Efficient Inference and Exploration for Reinforcement Learning Yi Zhu, Jing Dong, Henry Lam 26 Sep 2019 (modified: 24 Dec 2019) paper code

6. TabNet: Attentive Interpretable Tabular Learning Sercan O. Arik, Tomas Pfister 26 Sep 2019 (modified: 24 Dec 2019) paper code

7. Regularization Learning Networks: Deep Learning for Tabular Datasets Ira Shavitt, Eran Segal 2018 (modified: 17 Jul 2019) code code

8. TabNN: A Universal Neural Network Solution for Tabular Data Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, Tie-Yan Liu 28 Sep 2018 (modified: 21 Dec 2018) paper code

9. DORA The Explorer: Directed Outreaching Reinforcement Action-Selection Lior Fox, Leshem Choshen, Yonatan Loewenstein 16 Feb 2018 (modified: 22 Feb 2018) paper code

10. Mask Scoring R-CNN. Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, Xinggang Wang 2019 (modified: 03 Mar 2020) paper code

11. Temporal Anomaly Detection: Calibrating the Surprise. Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer 2019 (modified: 03 Mar 2020) paper code

12. Automatic Fusion of Segmentation and Tracking Labels Cem Emre Akbas, Vladimír Ulman, Martin Maska, Florian Jug, Michal Kozubek 2018 (modified: 03 Mar 2020) paper code

13. Viewport Proposal CNN for 360deg Video Quality Assessment. Chen Li, Mai Xu, Lai Jiang, Shanyi Zhang, Xiaoming Tao 2019 (modified: 03 Mar 2020) paper code

NLU

1. Incorporating BERT into Neural Machine Translation Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tieyan Liu 26 Sep 2019 (modified: 11 Mar 2020) paper code

2. TabFact: A Large-scale Dataset for Table-based Fact Verification Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang 26 Sep 2019 (modified: 11 Mar 2020)paper code

3. Probing Emergent Semantics in Predictive Agents via Question Answering Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Alden Hung, Josh Abramson, Arun Ahuja, Stephen Clark, Greg Wayne, Felix Hill 26 Sep 2019 (modified: 24 Dec 2019) paper code

4. Reproduction of Baselines on Label-Distribution-Aware Margin Loss and Deferred Reweighting Schedule Xinyu He, Hehuimin Cheng, Vitaly Kondulukov 29 Dec 2019 (modified: 29 Dec 2019) paper code

5. [RE] Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks Xueqing Zhao, Jinsong Zhang, Hao Sun 29 Dec 2019 (modified: 29 Dec 2019) paper code.

6. Reproducibility of "Augmented Neural ODEs" Henry Ho, Luca Zarow, Eric Zimmermann 29 Dec 2019 (modified: 29 Dec 2019) paper code

7. Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update Anonymous 02 Dec 2019 (modified: 08 Jan 2020) paper code

8. [Re] Better transfer learning with inferred successor maps Matthew Slivinski, Alana Jaskir, Aaron Traylor 02 Dec 2019 (modified: 29 Dec 2019) paper code

9. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution Antonio Matosevic, Eliisabet Hein, Francesco Nuzzo 02 Dec 2019 (modified: 29 Dec 2019) paper data

10. [Re] Exact Combinatorial Optimization with Graph Convolutional Neural Networks Audrey-Anne Guindon, Lourdes Crivelli 02 Dec 2019 (modified: 29 Dec 2019) paper data

11. Reproducing “Towards Interpretable ReinforcementLearning Using Attention Augmented Agents” Charles Lovering, Sam Lobel, Denizalp Goktas, Kweku Kwegyir-Aggrey, Albert Webson 02 Dec 2019 (modified: 29 Dec 2019) paper data

12. Lookahead Optimizer: k steps forward, 1 step back Rithwik Kukunuri, Shivji Bhagat 02 Dec 2019 (modified: 29 Dec 2019) paper code

NLI

1. Learning Credal Sum Product Networks Anonymous 15 Feb 2020 (modified: 15 Feb 2020) paper code

2. Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence Chi Sun, Luyao Huang, Xipeng Qiu 2019 (modified: 03 Mar 2020) paper code

3. Auto-Encoding Scene Graphs for Image Captioning. Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai 2019 (modified: 03 Mar 2020) paper code

4. CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling. Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei Li 2019 (modified: 03 Mar 2020) paper code

5. Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Mikel Artetxe 25 Sep 2019 paper code

6. Natural Language Inference with External Knowledge Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen 16 Feb 2018 (modified: 16 Feb 2018)paper code

7. Goten: GPU-Outsourcing Trusted Execution of Neural Network Training and Prediction Lucien K.L. Ng, Sherman S.M. Chow, Anna P.Y. Woo, Donald P. H. Wong, Yongjun Zhao 26 Sep 2019 (modified: 24 Dec 2019) paper code

8. PopSGD: Decentralized Stochastic Gradient Descent in the Population Model Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh. 26 Sep 2019 (modified: 24 Dec 2019) paper code

9. ADAPTING PRETRAINED LANGUAGE MODELS FOR LONG DOCUMENT CLASSIFICATION Matthew Lyle Olson, Lisa Zhang, Chun-Nam Yu 26 Sep 2019 (modified: 24 Dec 2019) paper code

10. GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation Jiawei Zhang, Lin Meng 26 Sep 2019 (modified: 24 Dec 2019) paper code

11. Improving Confident-Classifiers For Out-of-distribution Detection Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, Krzysztof Czarnecki 26 Sep 2019 (modified: 24 Dec 2019) paper code

12. Self-Supervised State-Control through Intrinsic Mutual Information Rewards Rui Zhao, Volker Tresp, Wei Xu 26 Sep 2019 (modified: 24 Dec 2019) paper code

13. On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks Michela Paganini, Jessica Forde 26 Sep 2019 (modified: 24 Dec 2019) paper code

14. Encoder-Agnostic Adaptation for Conditional Language Generation Zachary M. Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann, Alexander M. Rush 26 Sep 2019 (modified: 24 Dec 2019)paper code

15. Zero-Shot Out-of-Distribution Detection with Feature Correlations Chandramouli S Sastry, Sageev Oore 26 Sep 2019 (modified: 24 Dec 2019) paper code

16. Learning Credal Sum Product Networks 15 Feb 2020 (modified: 15 Feb 2020)paper code

Time Series:

2018

1. PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks . Jiaxiang Wu, Yao Zhang, Haoli Bai, Huasong Zhong, Jinlong Hou, Wei Liu, Wenbing Huang, Junzhou Huang paper code

2. Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation. Roman Föll, Bernard Haasdonk, Markus Hanselmann, Holger Ulmer paper code

3. Reconstructing evolutionary trajectories of mutations in cancer. Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit Deshwar, Nil Sahin, Quaid Morris paper code

4. Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control. Dmitry Kangin, Nicolas Pugeault paper code

2019

1. Reproducibility Challenge: Meta-LearningRepresentations for Continual Learning. Mihaela Georgieva Stoycheva, Sergio Liberman Bronfman, Konstantinos Saitas Zarkias paper code

2. Reproducibility of "Augmented Neural ODEs". Henry Ho, Luca Zarow, Eric Zimmermann paper code

3. Unsupervised Scalable Representation Learning for Multivariate Time Series. Felix Liljefors, Mohammad Moein Sorkhei, Sofia Broomé paper code

4. Neural Tangents: Fast and Easy Infinite Neural Networks in Python. Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz paper code

2020

1. Optimistic Exploration even with a Pessimistic Initialisation. Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson paper code

2. Kotlin∇: A shape-safe DSL for differentiable programming. Breandan Considine, Michalis Famelis, Liam Paull paper code

3. A Wild Bootstrap for Degenerate Kernel Tests . Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton paper code

4. Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models. Hugh Chen, Scott Lundberg, Gabe Erion, Su-In Lee paper code

5. ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees. Hao He, Hao Wang, Guang-He Lee, Yonglong Tian paper code

6. Interpolation-Prediction Networks for Irregularly Sampled Time Series. Satya Narayan Shukla, Benjamin Marlin paper code

7. Practical lossless compression with latent variables using bits back coding. James Townsend, Thomas Bird, David Barber paper code

8. mFLICA: An R package for Inferring Leadership of Coordination From Time Series. Chainarong Amornbunchornvej. paper

9. Extracting correlations in earthquake time series using complex network analysis. Sumanta Kundu, Anca Opris, Yohei Yukutake, and Takahiro Hatano paper

10. Pivotal tests for relevant differences in the second order dynamics of functional time series. Anne van Delft and Holger Dette paper

11. Extreme expectile estimation for heavy-tailed time series. Simone A. Padoan and Gilles Stupfler paper
    Bootstrap Prediction Bands for Functional Time Series. Efstathios Paparoditis, Han Lin Shang paper

12. Forecasting count data using time series model with exponentially decaying covariance structure. Soudeep Deb paper

13. TSInsight: A local-global attribution framework for interpretability in time-series data. Shoaib Ahmed Siddiqui, Dominique Mercier, Andreas Dengel, and Sheraz Ahmed paper

14. The fractal dimension of music: Melodic contours and time series of pitch. Maria H. Niklasson1, and Gunnar A. Niklasson paper

15. ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series. Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran paper

16. Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units. Zina Ibrahim, and Honghan Wu, and Richard Dobson paper

17. From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. Henning Lange, Steven L. Brunton, J. Nathan Kutz paper

18. A polynomial time algorithm to compute the connected tree-width of a series-parallel graph∗. Christophe Paul, Guillaume Mescoff, Dimitrios M. Thilikos paper

19. ANOMALY DETECTION IN UNIVARIATE TIME-SERIES: A SURVEY ON THE STATE-OF-THE-ART. Mohammad Braei, Dr.-Ing. Sebastian Wagner paper

20. Discrete orthogonal polynomials as a tool for detection of small anomalies of time series:a case study of GPS final orbits. S. P. Tsarev and A. A. Kytmanov paper

21. Adversarial Attacks on Multivariate Time Series. Samuel Harford, Fazle Karim, and Houshang Darabi paper

22. Difference Attention Based Error Correction LSTM Model for Time Series Prediction. Yuxuan Liu, Jiangyong Duan and Juan Meng paper

23. A correspondence between temporal correlations in time series, inverse problems, and the Spherical Model. Riccardo Marcaccioli and Giacomo Livan paper

24. Correlated daily time series and forecasting in the M4 competition. Anti Ingela, Novin Shahroudia, Markus K¨angseppa, Andre T¨attara, Viacheslav Komisarenkoa, Meelis Kulla paper

25. How the world’s collective attention is being paid to a pandemic:COVID-19 related 1-gram time series for 24 languages on Twitter. Thayer Alshaabi, Michael V. Arnold, Joshua R. Minot, Jane Lydia Adams, David Rushing Dewhurst, Andrew J. Reagan, Roby Muhamad, Christopher M. Danforth, and Peter Sheridan Dodds paper

26. ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series. Steven Elsworth · Stefan Guttel paper

27. Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing (Technical Report). Aoqian Zhang, Shaoxu Song, Jianmin Wang, Philip S. Yu paper

28. WHEN RAMANUJAN MEETS TIME-FREQUENCY ANALYSIS IN COMPLICATED TIME SERIES ANALYSIS. ZIYU CHEN AND HAU-TIENG WU papepr

29. Stationarity of Time-Series on Graph: A Generalized Approach via Transition Invariance. Amin Jalili paper

30. Financial Time Series Representation Learning. Philippe Chatigny, Jean-Marc Patenaude, Shengrui Wang paper

31. Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks. Bernardo Perez Orozco,and Stephen J. Roberts paper

32. Scalable Deployment of AI Time-series Models for IoT. Bradley Eck , Francesco Fusco , Robert Gormally , Mark Purcell , Seshu Tirupathi paper

33. Time series and machine learning to forecast the water quality from satellite data . Maryam R. Al Shehhi and Abdullah Kaya paper

34. On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions. Shaojun Guo and Xinghao Qiao paper

35. Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction. Satya Narayan Shukla, Benjamin M. Marlin paper

36. A time series method to analyze incidence pattern and estimate reproduction number of COVID-19. Soudeep Deb, Manidipa Majumdar paper

37. A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting. Marcelo Ruas, Alexandre Street, Cristiano Fernandes paper

38. Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting. Anirban Chatterjee, Subhadip Paul, Uddipto Dutta, Smaranya Dey paper

39. IMPROVING IRREGULARLY SAMPLED TIME SERIES LEARNING WITH DENSE DESCRIPTORS OF TIME. Rafael T. Sousa, Lucas A. Pereira, Anderson S. Soares paper

40. A comparison of Hurst exponent estimators in long-range dependent curve time series. Han Lin Shang paper

41. On neural architectures for astronomical time-series classification. Sara Jamal and Joshua S. Bloom paper

42. Modeling of Multisite Precipitation Occurrences Using Latent Gaussian-based Multivariate Binary Response Time Series. Hsien-Wei Chen paper

43. Construe: a software solution for the explanation-based interpretation of time series. T. Teijeiroa, P. F´elixb paper

44. Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting. Rosa Candela, Pietro Michiardi, Maurizio Filippone, and Maria A. Zuluaga paper

45. A Persistent Homology Approach to Time Series Classification. Yu-Min Chung, William Cruse, and Austin Lawson paper

46. Statistical Inference for High Dimensional Panel Functional Time Series. Zhou Zhou and Holger Dette paper

47. Time Series Forecasting Using LSTM Networks:A Symbolic Approach. Steven Elsworth and Stefan Guttel paper

48. Simulation of long-term time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step? Luis Ramirez Camargo, Johannes Schmidt paper

49. A Time Series Approach To Player Churn and Conversion in Videogames. Ana Fernández del Río, Anna Guitart and África Periánez paper

50. FORECASTING IN MULTIVARIATE IRREGULARLY SAMPLED TIME SERIES WITH MISSING VALUES. Shivam Srivastava, Prithviraj Sen, Berthold Reinwald paper

Self supervised learning

2019

1. UNSUPERVISED FEW-SHOT LEARNING VIA SELFSUPERVISED TRAINING. Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu paper

2. Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations. Shin’ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda paper

3. NEURAL OUTLIER REJECTION FOR SELF-SUPERVISED KEYPOINT LEARNING. Jiexiong Tang, Hanme Kim, Vitor Guizilini, Sudeep Pillai, Rares Ambrus paper

4. Multimodal Self-Supervised Learning for Medical Image Analysis. Aiham Taleb, Christoph Lippert, Tassilo Klein, and Moin Nabi papaer

5. Self-Supervised 3D Keypoint Learning for Ego-motion Estimation. Jiexiong Tang, Rares, Ambrus, Vitor Guizilini, Sudeep Pillai, Hanme Kim, Adrien Gaidon paper

6. Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning. Jannik Zurn, Wolfram Burgard, and Abhinav Valada paper

7. Self-Supervised Learning of Video-Induced Visual Invariances. Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Xiaohua Zhai, Neil Houlsby, Sylvain Gelly, Mario Lucic paper

8. Self-Supervised Learning of Pretext-Invariant Representations. Ishan Misra, Laurens van der Maaten paper

9. Self-Supervised Learning by Cross-Modal Audio-Video Clustering. Humam Alwassel1, Dhruv Mahajan, Lorenzo Torresani, Bernard Ghanem, Du Tran paper

10. Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning. Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma paper

11. EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning. Xiao Wang, Daisuke Kihara, Jiebo Luo, and Guo-Jun Qi paper

12. AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups. Feng Lin, Haohang Xu, Houqiang Li, Hongkai Xiong, Guo-Jun Qi paper

2020

13. Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences. Longlong Jing, Yucheng Chen, Ling Zhang, Mingyi He, and Yingli Tian paper

14. Temporally Coherent Embeddings for Self-Supervised Video Representation Learning. Joshua Knights, Anthony Vanderkop, Daniel Ward, Olivia, Mackenzie-Ross, and Peyman Moghadam paper

15. SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction. Xuming Hu, Lijie Wen, Yusong Xu, Chenwei Zhang, Philip S. Yu paper

16. Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics. Simon Jenni, Hailin Jin, Paolo Favaro, University of Bern, Adobe Research paper

17. Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. Pengpeng Liu, Irwin King, Michael Lyu, Jia Xu paper

18. Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition. Chih-Hui Ho, Bo Liu, Tz-Ying Wu, Nuno Vasconcelos paper

19. Self-Supervised Learning for Domain Adaptation on Point-Clouds. Idan Achituve, Haggai Maron, and Gal Chechik paper

20. ATTENTION-BASED SELF-SUPERVISED FEATURE LEARNING FOR SECURITY DATA. I-Ta Lee, Manish Marwah, Martin Arlitt paper

21. Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning. Thiago M. Paixao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessando L. Koerich, Claudine Badue, Alberto F. De Souza, and Thiago Oliveira-Santos paper

22. Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels. Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A. Plummer, Stan Sclaroff, and Kate Saenko paper

23. Self-Supervised Discovering of Causal Features: Towards Interpretable Reinforcement Learning. Wenjie Shi, Shiji Song, Zhuoyuan Wang, Gao Huang paper

24. Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach. Kanata Suzuki, Yasuto Yokota, Yuzi Kanazawa and Tomoyoshi Takebayashi paper

25. Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning. Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex Bronstein paper

26. Self-Supervised Spatio-Temporal Representation Learning Using Variable Playback Speed Prediction. Hyeon Cho, Taehoon Kim, Hyung Jin Chang, and Wonjun Hwang paper

27. Self-Supervised Graph Representation Learning via Global Context Prediction. Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, Qinghua Zheng paper

28. Self-Supervised Object-Level Deep Reinforcement Learning. William Agnew, Pedro Domingos paper

29. A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment. Piotr Kicki, Tomasz Gawron, and Piotr Skrzypczyski paper

30. A Multi-view Perspective of Self-supervised Learning. Chuanxing Geng, Zhenghao Tan, Songcan Chen paper

31. MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation. Marvin Chancan´and Michael Milford paper

32. Self-Supervised Viewpoint Learning From Image Collections. Siva Karthik Mustikovela, Varun Jampani, Shalini De Mello, Sifei Liu, Umar Iqbal, Carsten Rother, Jan Kautz paper

33. Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision. Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa paper

34. SELF-SUPERVISED REPRESENTATION LEARNING FOR ULTRASOUND VIDEO. Jianbo Jiao, Richard Droste, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble paper

35. SEMANTICALLY-GUIDED REPRESENTATION LEARNING FOR SELF-SUPERVISED MONOCULAR DEPTH. Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon paper

36. Learning a Directional Soft Lane Affordance Model for Road Scenes. Using Self-Supervision, Robin Karlsson and Erik Sjoberg paper

37. Automatic Shortcut Removal for Self-Supervised Representation Learning. Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen paper

38. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. Gregory Kahn, Pieter Abbeel, Sergey Levine papepr

39. SELF-SUPERVISED LEARNING FOR AUDIO-VISUAL SPEAKER DIARIZATION. Yifan Ding, Yong Xu, Shi-Xiong Zhang, Yahuan Cong and Liqiang Wang paper

40. SELF-SUPERVISED ECG REPRESENTATION LEARNING FOR EMOTION RECOGNITION. Pritam Sarkar, Ali Etemad papepr

41. Deep Self-Supervised Representation Learning for Free-Hand Sketch. Peng Xu, Zeyu Song, Qiyue Yin, Yi-Zhe Song paper

42. MULTI-TASK SELF-SUPERVISED LEARNING FOR ROBUST SPEECH RECOGNITION. Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual, Pawel Swietojanski, Joao Monteiro, Jan Trmal, Yoshua Bengio paper

43. Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning. Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez paper

44. Self-supervised visual feature learning with curriculum. Vishal Keshav, Fabien Delattre paper

45. VISUALLY GUIDED SELF SUPERVISED LEARNING OF SPEECH REPRESENTATIONS. Abhinav Shukla, Konstantinos Vougioukas, Pingchuan Ma, Stavros Petridis, Maja Pantic paper

46. Self-Supervised Fast Adaptation for Denoising via Meta-Learning. Seunghwan Lee, Donghyeon Cho, Jiwon Kim, and Tae Hyun Kim paper

47. Few-shot Learning with Multi-scale Self-supervision. Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz paper

48. Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy. Jacky C.K. Chow, Steven K. Boyd, Derek D. Lichti, Janet L. Ronsky paper

49. Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows. Gavin S. Hartnett, Masoud Mohseni paper

50. Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning. Dezhao Luo, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, Weiping Wang paper

NLP

2018

1. Baseline: Strong, Extensible, Reproducible, Deep Learning Baselines for NLP. Daniel Pressel, Brian Lester, Sagnik Ray Choudhury, Matt Barta, Yanjie Zhao, Amy Hemmeter paper code

2019

1. Spherical Text Embedding. Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han papepr code

2. Spherical Text Embedding. Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han paper [code]https://github.com/yumeng5/Spherical-Text-Embedding)

3. Sampling Bias in Deep Active Classification: An Empirical Study. Ameya Prabhu, Charles Dognin, Maneesh Singh paper code

4. Named Entity Recognition in Tweets: An Experimental Study. Alan Ritter, Sam Clark, Mausam, Oren Etzioni code code

5. Open domain event extraction from twitter. Alan Ritter, Mausam, Oren Etzioni, Sam Clark paper code

6. An Open-source Framework for Multi-level Semantic Similarity Measurement. Mohammad Taher Pilehvar, Roberto Navigli paper code

7. TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP. Nils Rethmeier and Vageesh Kumar Saxena and Isabelle Augenstein paper

8. Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models? Lyan Verwimp, Jerome R. Bellegarda paper

9. Do NLP Models Know Numbers? Probing Numeracy in Embeddings. Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner paper

10. AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models. Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, Sameer Singh paper

11. ATTENTION INTERPRETABILITY ACROSS NLP TASKS. Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar paper

12. Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task. Kenton Murray, Brian DuSell, David Chiang paper

13. NLPExplorer: Exploring the Universe of NLP Papers. Monarch Parmar, Naman Jain, Pranjali Jain, P Jayakrishna Sahit, Soham Pachpande, Shruti Singh, and Mayank Singh paper

14. HUBERT UNTANGLES BERT TO IMPROVE TRANSFER ACROSS NLP TASKS. Mehrad Moradshahi, Hamid Palangi, Monica S. Lam, Paul Smolensky, Jianfeng Gao paper

15. The State of NLP Literature: A Diachronic Analysis of the ACL Anthology. Saif M. Mohammad paper

16. Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, Laura B. Kleiman paper

17. ERASER: A Benchmark to Evaluate Rationalized NLP Models. Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace paper

18. UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data. Arun Rajendran, Chiyu Zhang, Muhammad Abdul-Mageed paper

19. Principled Frameworks for Evaluating Ethics in NLP Systems. Shrimai Prabhumoye, Elijah Mayfield, Alan W Black paper

20. When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification. Shumin Deng, Ningyu Zhang, Zhanlin Sun, Jiaoyan Chen, Huajun Chen paper

21. Universal Adversarial Triggers for Attacking and Analyzing NLP. Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh paper

22. What’s Wrong with Hebrew NLP? And How to Make it Right. Reut Tsarfaty, Amit Seker, Shoval Sadde, Stav Klein paper

23. Normalyzing Numeronyms - A NLP approach. Avishek Garain, Sainik Kumar Mahata, Subhabrata Dutta paper

24. CFO: A Framework for Building Production NLP Systems. Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avirup Sil paper

25. Energy and Policy Considerations for Deep Learning in NLP. Emma Strubell Ananya Ganesh Andrew McCallum paper

26. A Just and Comprehensive Strategy for Using NLP to Address Online Abuse. David Jurgens, Eshwar Chandrasekharan, Libby Hemphill paper

27. System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications. Baohua Sun, Lin Yang, Michael Lin, Wenhan Zhang, Patrick Dong, Charles Young and Jason Dong paper

28. Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications. Wei Zhao, Haiyun Peng, Steffen Eger, Erik Cambria‡ and Min Yang paper

29. Automatic Generation of System Test Cases from Use Case Specifications: an NLP-based Approach. Chunhui Wang, Fabrizio Pastore, Arda Goknil, and Lionel C. Briand paper

30. You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP. Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, Raquel Fernandez paper

2020

1. On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models. Paul Michel, Xian Li, Graham Neubig, Juan Miguel Pino paper code

2. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Roland Vollgraf paper code

3. Approximating Word Ranking and Negative Sampling for Word Embedding. Guibing Guo, Shichang Ouyang, Fajie Yuan, Xingwei Wang paper code

4. Improving Neural Fine-Grained Entity Typing With Knowledge Attention. Ji Xin, Yankai Lin, Zhiyuan Liu, Maosong Sun paper code

5. Language Models as Knowledge Bases? Fabio Petroni, Tim Rocktaschel, ¨Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel paper code

6. Towards Faithfully Interpretable NLP Systems:How should we define and evaluate faithfulness? Alon Jacovi, Yoav Goldberg paper

7. Operationalizing the legal concept of ‘Incitement to Hatred’ as an NLP task. Frederike Zufall, Huangpan Zhang, Katharina Kloppenborg, Torsten Zesch paper

8. Orchestrating NLP Services for the Legal Domain. Julian Moreno-Schneider, Georg Rehm, Elena Montiel-Ponsoda, V´ıctor Rodr´ıguez-Doncel, Artem Revenko, Sotirios Karampatakis, Maria Khvalchik, Christian Sageder, Jorge Gracia, Filippo Maganza paper

9. Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks. Tosin P. Adewumi*, Foteini Liwicki & Marcus Liwicki paper

10. Parsing Thai Social Data: A New Challenge for Thai NLP. Sattaya Singkul, Borirat Khampingyot, Nattasit Maharattamalai, Supawat Taerungruang, Tawunrat Chalothorn paper

11. HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics. Pedro Alonso, Kumar Shridhar, Denis Kleyko, Evgeny Osipov, Marcus Liwicki paper

12. A Nepali Rule Based Stemmer and its performance on different NLP applications Pravesh Koirala, Aman Shakya paper

13. Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets Mona Jalal, Kate K. Mays, Lei Guo, and Margrit Betke paper

14. autoNLP: NLP Feature Recommendations for Text Analytics Applications Janardan Misra paper

15. FASTWORDBUG: A FAST METHOD TO GENERATE ADVERSARIAL TEXT AGAINST NLP APPLICATIONS. Dou Goodman, Lv Zhonghou & Wang Minghu paper

16. SemClinBr – a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks. Lucas Emanuel Silva e Oliveiraa, Ana Carolina Petersa, Adalniza Moura Pucca da Silvaa, Caroline P. Gebelucaa, Yohan Bonescki Gumiela, Lilian Mie Mukai Cinthoa, Deborah Ribeiro Carvalhoa, Sadid A. Hasanb, Claudia Maria Cabral Moro paper

17. Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling. Clarence Chen, Zachary Pardos paper

18. Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLP. Ying Xu, Xu Zhong, Antonio Jose Jimeno Yepes, Jey Han Lau paper

19. Dice Loss for Data-imbalanced NLP Tasks. Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu and Jiwei Li paper

20. R2DE: a NLP approach to estimating IRT parameters of newly generated questions. Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi paper

21. Evaluating NLP Models via Contrast Sets. Matt GardnerF, Yoav ArtziΓ Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou paper

22. PLAYING THE LOTTERY WITH REWARDS AND MULTIPLE LANGUAGES: LOTTERY TICKETS IN RL AND NLP. Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S. Morcos paper

GAN

2018

1. Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks Thang Vu, Tung M. Luu, Chang D. Yoo paper code

2. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, Jiri Matas paper code

3. HP-GAN: Probabilistic 3D Human Motion Prediction via GAN Emad Barsoum, John Kender, Zicheng Liu paper code

4. Image Synthesis with a Convolutional Capsule Generative Adversarial Network Cher Bass, Tianhong Dai, Benjamin Billot, Kai Arulkumaran, Antonia Creswell, Claudia Clopath, Vincenzo De Paola, Anil Anthony Bharath paper code

5. Image Synthesis with a Convolutional Capsule Generative Adversarial Network Cher Bass, Tianhong Dai, Benjamin Billot, Kai Arulkumaran, Antonia Creswell, Claudia Clopath, Vincenzo De Paola, Anil Anthony Bharath paper code

6. Diagnosing and Enhancing VAE Models Bin Dai, David Wipf paper code

7. The Unusual Effectiveness of Averaging in GAN Training Yasin Yaz{\i}c{\i}, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar paper code

8. The relativistic discriminator: a key element missing from standard GAN Alexia Jolicoeur-Martineau paper code

9. Generating Multiple Objects at Spatially Distinct Locations Tobias Hinz, Stefan Heinrich, Stefan Wermter paper code

10. The relativistic discriminator: a key element missing from standard GAN Alexia Jolicoeur-Martineau paper code

11. InstaGAN: Instance-aware Image-to-Image Translation Sangwoo Mo, Minsu Cho, Jinwoo Shin paper code

12. Whitening and Coloring Batch Transform for GANs Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe paper code

13. Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective Zhuoran Yang, Zuyue Fu, Kaiqing Zhang, Zhaoran Wang papere code

14. DEEP GRAPH TRANSLATION Xiaojie Guo, Lingfei Wu, Liang Zhao paper code

15. Brain MRI super-resolution using 3D generative adversarial networks. Irina Sánchez , Verónica Vilaplana paper

2019

1. Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou paper code

2. Unpaired Point Cloud Completion on Real Scans using Adversarial Training Xuelin Chen, Baoquan Chen, Niloy J. Mitra paper code

3. Unsupervised Video Summarization via Attention-Driven Adversarial Learning Evlampios E. Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris, Ioannis Patras paper code

4. Learning Implicit Fields for Generative Shape Modeling. Zhiqin Chen, Hao Zhang paper code

5. Sphere Generative Adversarial Network Based on Geometric Moment Matching. Sung Woo Park, Junseok Kwon paper code

6. Improving GAN with Neighbors Embedding and Gradient Matching. Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung paper code

7. FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery. Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee paper code

8. Consistency Regularization for Generative Adversarial Networks Han Zhang, Zizhao Zhang, Augustus Odena, Honglak Lee paper code

9. Non-Sequential Melody Generation Mitchell Billard, Robert Bishop, Moustafa Elsisy, Laura Graves, Antonina Kolokolova, Vineel Nagisetty, Zachary Northcott, Heather Patey paper code

10. Non-Sequential Melody Generation Mitchell Billard, Robert Bishop, Moustafa Elsisy, Laura Graves, Antonina Kolokolova, Vineel Nagisetty, Zachary Northcott, Heather Patey paper code

11. Imagining the Latent Space of a Variational Auto-Encoders Zezhen Zeng, Jonathon Hare, Adam Prügel-Bennett paper code

12. Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints Ning Yu, Larry Davis, Mario Fritz [paper]https://arxiv.org/pdf/1811.08180.pdf code

13. Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alex M. Bronstein paper code

14. Adaptive Generation of Unrestricted Adversarial Inputs Isaac Dunn, Hadrien Pouget, Tom Melham, Daniel Kroening paper code

15. Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin paper code

16. Generalized Zero-shot ICD Coding Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing paper code

17. TOWARDS FEATURE SPACE ADVERSARIAL ATTACK Qiuling Xu, Guanhong Tao, Siyuan Cheng, Lin Tan, Xiangyu Zhang paper code

18. Progressive Compressed Records: Taking a Byte Out of Deep Learning Data Michael Kuchnik, George Amvrosiadis, Virginia Smith paper code

19. Score and Lyrics-Free Singing Voice Generation Jen-Yu Liu, Yu-Hua Chen, Yin-Cheng Yeh, Yi-Hsuan Yang paper code

20. Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators Daniel Stoller, Sebastian Ewert, Simon Dixon paper code

21. UWGAN: UNDERWATER GAN FOR REAL-WORLD UNDERWATER COLOR RESTORATION AND DEHAZING Nan Wang, Yabin Zhou, Fenglei Han, Lichao Wan, Haitao Zhu, Yaojing Zheng paper code

22. Adversarial Lipschitz Regularization Dávid Terjék paper code

23. Language GANs Falling Short Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin paper code

24. RPGAN: random paths as a latent space for GAN interpretability Andrey Voynov, Artem Babenko paper code

25. BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS Faqiang Liu, Mingkun Xu, Guoqi Li, Jing Pei, Luping Shi paper code

26. LIA: Latently Invertible Autoencoder with Adversarial Learning Jiapeng Zhu, Deli Zhao, Bolei Zhou, Bo Zhang paper code

27. LIA: Latently Invertible Autoencoder with Adversarial Learning Jiapeng Zhu, Deli Zhao, Bolei Zhou, Bo Zhang paper code

28. Observational Overfitting in Reinforcement Learning Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur paper code

29. Language GANs Falling Short Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin paper code

30. Expected Information Maximization: Using the I-Projection for Mixture Density Estimation Philipp Becker, Oleg Arenz, Gerhard Neumann paper code

31. Expected Information Maximization: Using the I-Projection for Mixture Density Estimation Philipp Becker, Oleg Arenz, Gerhard Neumann paper code

32. Expected Information Maximization: Using the I-Projection for Mixture Density Estimation Philipp Becker, Oleg Arenz, Gerhard Neumann paper code

33. BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS Faqiang Liu, Mingkun Xu, Guoqi Li, Jing Pei, Luping Shi paper code

34. Implicit competitive regularization in GANs Florian Schaefer, Hongkai Zheng, Anima Anandkumar paper code

35. Progressive Augmentation of GANs Dan Zhang, Anna Khoreva paper code

36. Learning from Label Proportions with Generative Adversarial Networks Jiabin Liu, Bo Wang, Zhiquan Qi, YingJie Tian, Yong Shi paper code

37. Shape Features Improve General Model Robustness Chaowei Xiao, Mingjie Sun, Haonan Qiu, Han Liu, Mingyan Liu, Bo Li paper code

38. High Fidelity Speech Synthesis with Adversarial Networks Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan paper code

39. Small-GAN: Speeding up GAN Training using Core-Sets Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena paper code

40. Defense against Adversarial Examples by Encoder-Assisted Search in the Latent Coding Space Wenjing Huang, Shikui Tu, Lei Xu paper code

41. PAGANDA: An Adaptive Task-Independent Automatic Data Augmentation Boli Fang, Miao Jiang, Jerry Shen paper code

42. Asymmetric Generative Adversarial Networks for Image-to-Image Translation Hao Tang, Dan Xu, Hong Liu and Nicu Sebe paper

43. cGANs with Multi-Hinge Loss Ilya Kavalerov, Wojciech Czaja, Rama Chellappa paper

44. Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference Wei-An Lin, Yogesh Balaji, Pouya Samangouei, Rama Chellappa paper

45. Adversarial Fisher Vectors for Unsupervised Representation Learning Shuangfei Zhai Walter Talbott Carlos Guestrin Joshua M. Susskind paper

46. Alleviating Feature Confusion for Generative Zero-shot Learning Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, Zi Huang paper

47. Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks. Baris Gecer, Alexander Lattas, Stylianos Ploumpis, Jiankang Deng, Athanasios Papaioannou, Stylianos Moschoglou, and Stefanos Zafeiriou paper

48. Spectral Regularization for Combating Mode Collapse in GANs. Kanglin Liu, Wenming Tang, Ruitao Xie, and Guoping Qiu paper

49. Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays. Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, and Tolga Tasdizen paper

50. Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization. Md Mahfuzur Rahman Siddiquee, Zongwei Zhou1,3, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio, and Jianming Liang paper

51. AutoGAN: Neural Architecture Search for Generative Adversarial Networks. Xinyu Gong, Shiyu Chang, Yifan Jiang, Zhangyang Wang paper

52. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang paper

53. Cosmological N-body simulations: a challenge for scalable generative models. Nathana¨el Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann and Alexandre R´efr´egier paper

54. Progressive Perception-Oriented Network for Single Image Super-Resolution. Zheng Hui, Jie Li, Xinbo Gao, Senior Member, IEEE and Xiumei Wang paper

55. Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation. Bin Duanb, Wei Wang, Hao Tang, Hugo Latapie paper

56. CELLULAR STATE TRANSFORMATIONS USING GENERATIVE ADVERSARIAL NETWORKS. Colin Targonski, Benjamin T. Shealy, Melissa C. Smith, F. Alex Feltus paper

57. Adversarial Sub-sequence for Text Generation. Xingyuan Chen, Yanzhe Li, Peng Jin, Jiuhua Zhang, Xinyu Dai, Jiajun Chen, Gang Song paper

58. Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation. Mihai Marian Puscas, Dan Xu, Andrea Pilzer, Niculae Sebe paper

59. Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation. Hao Tang, Dan Xu, Nicu Sebe, Yan Yan paper

60. Diagnosing and Enhancing VAE Models. Bin Dai, David Wipf paper

61. Virtual Conditional Generative Adversarial Networks. Haifeng Shi Guanyu Cai Yuqin Wang Shaohua Shang Lianghua He paper

62. CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation. Kishan Babu Kancharagunta and Shiv Ram Dubey paper

63. TF-REPLICATOR: DISTRIBUTED MACHINE LEARNING FOR RESEARCHERS. Peter Buchlovsky, David Budden, Dominik Grewe, Chris Jones, John Aslanides, Frederic Besse, Andy Brock, Aidan Clark, Sergio Gomez Colmenarejo, Aedan Pope, Fabio Viola, Dan Belov paper

2020

1. 4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model anonymus paper code

2. Rubeus-GAN: Attacking class imbalance via conditioned generation. A medical imaging perspective Anonymous paper code

3. Unsupervised Video Summarization via Attention-Driven Adversarial Learning Evlampios E. Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris, Ioannis Patras paper code

4. Discriminative region proposal adversarial network for high-quality image-to-image translation Chao Wang, Wenjie Niu, Yufeng Jiang, Haiyong Zheng, Zhibin Yu, Zhaorui Gu, Bing Zheng paper code

5. FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery Krishna Kumar Singh paper code

6. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily Denton∗, Soumith Chintala, Arthur Szlam, Rob Fergus paper

7. Regression via Implicit Models and Optimal Transport Cost Minimization. Saurav Manchanda, Khoa Doan, Pranjul Yadav, S. Sathiya Keerthi paper

8. PCSGAN: Perceptual Cyclic-Synthesized Generative Adversarial Networks for Thermal and NIR to Visible Image Transformation Kancharagunta Kishan Babu and Shiv Ram Dubey paper

9. Image Fine-grained Inpainting Zheng Hui, Jie Li, Xiumei Wang, and Xinbo Gao∗ School of Electronic Engineering, Xidian University, Xi’an, China paper

10. CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records Amirsina Torfi, Edward A. Fox paper

11. S2OMGAN: Shortcut from Remote Sensing Images to Online Maps Xu Chen, Songqiang Chen, Tian Xu, Bangguo Yin, Jian Peng, Xiaoming Mei, Haifeng Li∗ paper

12. Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs Sangwoo Mo, Minsu Cho, Jinwoo Shin paper

13. UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing Nan Wang, Yabin Zhou, Fenglei Han, Haitao Zhu, Yaojing Zheng paper

14. MineGAN: effective knowledge transfer from GANs to target domains with few images Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Faha Shahbaz Khan, Joost van de Weijer paper

15. AdversarialNAS: Adversarial Neural Architecture Search for GANs. Chen Gao, Yunpeng Chen, Si Liu, Zhenxiong Tan, Shuicheng Yan paper

16. Noise Robust Generative Adversarial Networks Takuhiro Kaneko, Tatsuya Harada paper

17. Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, Ngai-Man Cheungpaper

18. Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation. Runfa Chen, Wenbing Huang, Binghui Huang, Fuchun Sun, Bin Fang paper

19. A NEURO-AI INTERFACE FOR EVALUATING GENERATIVE ADVERSARIAL NETWORKS. Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward ´& Graham Healy paper

20. Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo paper

21. Feature Quantization Improves GAN Training. Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen paper

22. Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy. ZHENGWEI WANG, QI SHE, QI SHE paper

23. Fast Underwater Image Enhancement for Improved Visual Perception. Md Jahidul Islam, Youya Xia and Junaed Sattar paper

VAE

2018

1. Practical lossless compression with latent variables using bits back coding James Townsend, Thomas Bird, David Barber paper code

2. Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan paper code

3. Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani paper code

4. Practical lossless compression with latent variables using bits back coding James Townsend, Thomas Bird, David Barber paper code

5. GO Gradient for Expectation-Based Objectives Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin paper code

6. Practical lossless compression with latent variables using bits back coding James Townsend, Thomas Bird, David Barber paper code

7. Switching Linear Dynamics for Variational Bayes Filtering Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt paper code

8. ISA-VAE: Independent Subspace Analysis with Variational Autoencoders Jan Stühmer, Richard Turner, Sebastian Nowozin paper code

9. Generative Models from the perspective of Continual Learning Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Jean-François Goudou, David Filliat paper code

10. HyperGAN: Exploring the Manifold of Neural Networks Neale Ratzlaff, Li Fuxin paper code

11. STCN: Stochastic Temporal Convolutional Networks Emre Aksan, Otmar Hilliges paper code

12. Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer David Berthelot*, Colin Raffel*, Aurko Roy, Ian Goodfellow paper code

13. Variational Sparse Coding Francesco Tonolini, Bjorn Sand Jensen, Roderick Murray-Smith paper code

14. Practical lossless compression with latent variables using bits back coding James Townsend, Thomas Bird, David Barber paper code

15. Diagnosing and Enhancing VAE Models Bin Dai, David Wipf paper code

2019

1. Disentangling and learning robust representations with naturual clustering . Antoran, Miguel paper

2. Inherent tradeoffs in learning fair representations. Zhao, Gordon paper

3. Affine variational autoencoders: an efficient approach for improving generalization and robustness to distribution shift. Bidart, Wong paper

4. Learning deep controllable and structured representations for image synthesis, structured prediction and beyond. Yan paper

5. Continual unsupervised representation learning . Rao, Visin, Rusu, The, Pascanu, Hadsell paper

6. Group-based learning of disentangled representations with generalizability for novel contents. Hosoya paper

7. Task-Conditioned variational autoencoders for learning movement primitives. Noseworthy, Paul, Roy, Park, Roy paper

8. Multimodal generative models for compositional representation learning. Wu, Goodman paper

9. dpVAEs: fixing sample generation for regularized VAEs. Bhalodia, Lee, Elhabian paper

10. From variational to deterministic autoencoders. Ghosh, Sajjadi, Vergai, Black, Scholkopf paper

11. Learning representations by maximizing mutual information in variational autoencoder. Rezaabad, Vishwanath (https://arxiv.org/pdf/1912.13361.pdf )

12. Disentangled representation learning with Wasserstein total correlation. Xiao, Wang paper

13. Wasserstein dependency measure for representation learning. Ozair, Lynch, Bengio, van den Oord, Levine, Sermanent paper

14. GP-VAE: deep probabilistic time series imputation. Fortuin, Baranchuk, Ratsch, Mandt paper [code] (https://github.com/ratschlab/GP-VAE)

15. Gated Variational Autoencoders: Incorporating weak supervision to encourage disentanglement. Vowels, Camgoz, Bowden paper

16. An introduction to variational autoencoders. Kingma, Welling paper

17. Adaptive density estimation for generative models Lucas, Shmelkov, Schmid, Alahari, Verbeek paper

18. Data efficient mutual information neural estimator Lin, Sur, Nastase, Divakaran, Hasson, Amer paper

19. RecVAE: a new variational autoencoder for Top-N recommendations with implicit feedback. Shenbin, Alekseev, Tutubalina, Malykh, Nikolenko paper

20. Vibration signal generation using conditional variational autoencoder for class imbalance problem. Ko, Kim, Kong, Lee, Youn paper

21. The usual suspects? Reassessing blame for VAE posterior collapse. Dai, Wang, Wipf paper

22. What does the free energy principle tell us about the brain? Gershman paper

23. Sub-band vector quantized variational autoencoder for spectral envelope quantization. Srikotr, Mano paper

24. A variational-sequential graph autoencoder for neural performance prediction. Friede, Lukasik, Stuckenschmidt, Keuper paper

25. Explicit disentanglement of appearance and perspective in generative models. Skafte, Hauberg paper

26. Disentangled behavioural representations. Dezfouli, Ashtiani, Ghattas, Nock, Dayan, Ong paper

27. Learning disentangled representations for robust person re-identification. Eom, Ham paper

28. Towards latent space optimality for auto-encoder based generative models. Mondal, Chowdhury, Jayendran, Singla, Asnani, AP paper

29. Don't blame the ELBO! A linear VAE perspective on posterior collapse. Lucas, Tucker, Grosse, Norouzi paper

30. Bridging the ELBO and MMD. Ucar paper

31. Learning disentangled representations for counterfactual regression. Hassanpour, Greiner paper

32. Learning disentangled representations for recommendation. Ma, Zhou, Cui, Yang, Zhu paper

33. A vector quantized variational autoencoder (VQ-VAE) autoregressive neural F0 model for statistical parametric speech synthesis. Wang, Takaki, Yamagishi, King, Tokuda paper

34. Diversity-aware event prediction based on a conditional variational autoencoder with reconstruction. Kiyomaru, Omura, Murawaki, Kawahara, Kurohashi paper

35. Learning multimodal representations with factorized deep generative models. Tsai, Liang, Zadeh, Morency, Salakhutdinov paper

36. High-dimensional nonlinear profile monitoring based on deep probabilistic autoencoders. Sergin, Yan paper

37. Leveraging directed causal discovery to detect latent common causes. Lee, Hart, Richens, Johri paper

38. Robust discrimination and generation of faces using compact, disentangled embeddings. Browatzki, Wallraven paper

39. Coulomb Autoencoders. Sansone, Ali, Sun paper

40. Contrastive learning of structured world models. Kipf, Pol, Welling paper

41. No representation without transformation. Giannone, Masci, Osendorfer paper

42. Neural density estimation. Papamakarios paper

43. Variational autoencoder-based approach for rail defect identification. Wei, Ni paper

44. Variational learning with disentanglement-pytorch. Abdi, Abolmaesumi, Fels paper

45. PVAE: learning disentangled representations with intrinsic dimension via approximated L0 regularization. Shi, Glocker, Castro paper

46. Mixed-curvature variational autoencoders. Skopek, Ganea, Becigneul paper

47. Continuous hierarchical representations with poincare variational autoencoders. Mathieu, Le Lan, Maddison, Tomioka paper

48. VIREL: A variational inference framework for reinforcement learning. Fellows, Mahajan, Rudner, Whiteson paper

49. Disentangling video with independent prediction. Whitney, Ferguspaper

50. Disentangling state space representations Miladinovic, Gondal, Scholkopf, Buhmann, Bauer paper

51. Likelihood conribution based multi-scale architecture for generative flows. Das, Abbeel, Spanos paper

52. AlignFlow: cycle consistent learning from multiple domains via normalizing flows Grover, Chute, Shu, Cao, Ermon paper

53. IB-GAN: disentangled representation learning with information bottleneck GAN. Jeon, Lee, Kim paper

54. Learning hierarchical priors in VAEs. Klushyn, Chen, Kurle, Cseke, van der Smagt paper

55. ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. Yildiz, Heinonen, Lahdesmaki paper

56. Explicitly disentangling image content from translation and rotation with spatial-VAE. Bepler, Zhong, Kelley, Brignole, Berger paper

57. A primal-dual link between GANs and autoencoders. Husain, Nock, Williamson paper

58. Exact rate-distortion in autoencoders via echo noise. Brekelmans, Moyer, Galstyan, ver Steeg paper

59. Direct optimization through arg max for discrete variational auto-encoder. Lorberbom, Jaakkola, Gane, Hazan paper

60. Semi-implicit graph variational auto-encoders. Hasanzadeh, Hajiramezanali, Narayanan, Duffield, Zhou, Qian paper

61. The continuous Bernoulli: fixing a pervasive error in variational autoencoders. Loaiza-Ganem, Cunningham paper

62. Provable gradient variance guarantees for black-box variational inference. Domke paper

63. Conditional structure generation through graph variational generative adversarial nets. Yang, Zhuang, Shi, Luu, Li paper

64. Scalable spike source localization in extracellular recordings using amortized variational inference. Hurwitz, Xu, Srivastava, Buccino, Hennig paper

65. A latent variational framework for stochastic optimization. Casgrain paper

66. MAVEN: multi-agent variational exploration. Mahajan, Rashid, Samvelyan, Whiteson paper

67. Variational graph recurrent neural networks. Hajiramezanali, Hasanzadeh, Narayanan, Duffield, Zhou, Qian paper

68. The thermodynamic variational objective. Masrani, Le, Wood paper

69. Variational temporal abstraction. Kim, Ahn, Bengio paper

70. Exploiting video sequences for unsupervised disentangling in generative adversarial networks. Tuesca, Uzal paper

71. Couple-VAE: mitigating the encoder-decoder incompatibility in variational text modeling with coupled deterministic networks. paper

72. Variational mixture-of-experts autoencoders for multi-modal deep generative models. Shi, Siddharth, Paige, Torr paper

73. Invertible convolutional flow. Karami, Schuurmans, Sohl-Dickstein, Dinh, Duckworth paper

74. Implicit posterior variational inference for deep Gaussian processes. Yu, Chen, Dai, Low, Jaillet paper

75. MaCow: Masked convolutional generative flow. Ma, Kong, Zhang, Hovy paper

76. Residual flows for invertible generative modeling. Chen, Behrmann, Duvenaud, Jacobsen paper

77. Discrete flows: invertible generative models of discrete data. Tran, Vafa, Agrawal, Dinh, Poole paper

78. Re-examination of the role of latent variables in sequence modeling. Lai, Dai, Yang, Yoo paper

79. Learning-in-the-loop optimization: end-to-end control and co-design of soft robots through learned deep latent representations. Spielbergs, Zhao, Hu, Du, Matusik, Rus paper

80. Triad constraints for learning causal structure of latent variables. Cai, Xie, Glymour, Hao, Zhang paper

81. Disentangling influence: using disentangled representations to audit model predictions. Marx, Phillips, Friedler, Scheidegger, Venkatasubramanian paper

82. Symmetry-based disentangled representation learning requires interaction with environments. Caselles-Dupre, Ortiz, Filliat paper

83. Weakly supervised disentanglement with guarantees. Shu, Chen, Kumar, Ermon, Poole paper

84. Demystifying inter-class disentanglement. Gabbay, Hoshen paper

85. Spectral regularization for combating mode collapse in GANs. Liu, Tang, Xie, Qiu paper

86. Geometric disentanglement for generative latent shape models. Aumentado-Armstrong, Tsogkas, Jepson, Dickinson paper

87. Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. Li, Lin, Lin, Wang paper

88. Identity from here, pose from there: self-supervised disentanglement and generation of objects using unlabeled videos. Xiao, Liu, Lee paper

89. Content and style disentanglement for artistic style transfer. Kotovenko, Sanakoyeu, Lang, Ommer paper

90. Unsupervised robust disentangling of latent characteristics for image synthesis. Esser, Haux, Ommer paper

91. LADN: local adversarial disentangling network for facial makeup and de-makeup. Gu, Wang, Chiu, Tai, Tang paper

92. Video compression with rate-distortion autoencoders. Habibian, van Rozendaal, Tomczak, Cohen paper

93. Variable rate deep image compression with a conditional autoencoder. Choi, El-Khamy, Lee paper

94. Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. Gong, Liu, Le, Saha paper

95. AVT: unsupervise d learning of transformation equivariant representations by autoencoding variational transformations. Qi, Zhang, Chen, Tian paper

96. Deep clustering by Gaussian mixture variational autoencoders with graph embedding. Yang, Cheung, Li, Fang paper

97. Variational adversarial active learning. Sinha, Ebrahimi, Darrell paper

98. Variational few-shot learning. Zhang, Zhao, Ni, Xu, Yang paper

99. Multi-angle point cloud-VAE: unsupervised feature learning for 3D point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. Han, Wang, Liu, Zwicker paper

100. LayoutVAE: stochastic scene layout generation from a label set. Jyothi, Durand, He, Sigal, Mori paper

101. VV-NET: Voxel VAE Net with group convolutions for point cloud segmentation. Meng, Gao, Lai, Manocha paper

102. Bayes-Factor-VAE: hierarchical bayesian deep auto-encoder models for factor disentanglement. Kim, Wang, Sahu, Pavlovic paper

103. Robust ordinal VAE: Employing noisy pairwise comparisons for disentanglement. Chen, Batmanghelich paper

104. Evaluating disentangled representations. Sepliarskaia, A. and Kiseleva, J. and de Rijke, M. paper

105. A stable variational autoencoder for text modelling. Li, R. and Li, X. and Lin, C. and Collinson, M. and Mao, R. paper

106. Hamiltonian generative networks. Toth, Rezende, Jaegle, Racaniere, Botev, Higgins paper

107. LAVAE: Disentangling location and appearance. Dittadi, Winther paper

108. Interpretable models in probabilistic machine learning. Kim paper

109. Disentangling speech and non-speech components for building robust acoustic models from found data. Gurunath, Rallabandi, Black paper

110. Joint separation, dereverberation and classification of multiple sources using multichannel variational autoencoder with auxiliary classifier. Inoue, Kameoka, Li, Makino paper

111. SuperVAE: Superpixelwise variational autoencoder for salient object detection. Li, Sun, Guo paper

112. Implicit discriminator in variational autoencoder. Munjal, Paul, Krishnan paper

113. TransGaGa: Geometry-aware unsupervised image-to-image translation. Wu, Cao, Li, Qian, Loy paper

114. Variational attention using articulatory priors for generating code mixed speech using monolingual corpora. Rallabandi, Black. paper

115. One-class collaborative filtering with the queryable variational autoencoder. Wu, Bouadjenek, Sanner. paper

116. Predictive auxiliary variational autoencoder for representation learning of global speech characteristics. Springenberg, Lakomkin, Weber, Wermter. paper

117. Data augmentation using variational autoencoder for embedding based speaker verification. Wu, Wang, Qian, Yu paper

118. One-shot voice conversion with disentangled representations by leveraging phonetic posteriograms. Mohammadi, Kim. paper

119. EEG-based adaptive driver-vehicle interface using variational autoencoder and PI-TSVM. Bi, Zhang, Lian paper

120. Neural gaussian copula for variational autoencoder Wang, Wang paper

121. Enhancing VAEs for collaborative filtering: Flexible priors and gating mechanisms. Kim, Suh paper

122. Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. Wang, Wang paper

123. Disentanglement with hyperspherical latent spaces using diffusion variational autoencoders. Rey paper

124. Learning deep representations by mutual information estimation and maximization. Hjelm, Fedorov, Lavoie-Marchildon, Grewal, Bachman, Trischler, Bengio paper code

125. Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation. Vladymyrov, Ariga paper

126. Real time trajectory prediction using conditional generative models. Gomez-Gonzalez, Prokudin, Scholkopf, Peters paper

127. Disentanglement challenge: from regularization to reconstruction. Qiao, Li, Cai paper

128. Improved disentanglement through aggregated convolutional feature maps. Seitzer paper

129. Linked variational autoencoders for inferring substitutable and supplementary items. Rakesh, Wang, Shu paper

130. On the fairness of disentangled representations. Locatello, Abbati, Rainforth, Bauer, Scholkopf, Bachem paper

131. Learning robust representations by projecting superficial statistics out. Wang, He, Lipton, Xing paper

132. Understanding posterior collapse in generative latent variable models. Lucas, Tucker, Grosse, Norouzi paper

133. On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset. Gondal, Wuthrich, Miladinovic, Locatello, Breidt, Volchkv, Akpo, Bachem, Scholkopf, Bauer paper code

134. DIVA: domain invariant variational autoencoder. Ilse, Tomczak, Louizos, Welling paper code

135. Comment: Variational Autoencoders as empirical Bayes. Wang, Miller, Blei paper

136. Fast MVAE: joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier. Li, Kameoka, Makino paper

137. Reweighted expectation maximization. Dieng, Paisley paper code

138. Semisupervised text classification by variational autoencoder. Xu, Tan paper

139. Learning deep latent-variable MRFs with amortized Bethe free-energy minimization. Wiseman paper

140. Contrastive variational autoencoder enhances salient features. Abid, Zou paper code

141. Learning latent superstructures in variational autoencoders for deep multidimensional clustering. Li, Chen, Poon, Zhang paper

142. Tighter variational bounds are not necessarily better. Rainforth, Kosiorek, Le, Maddison, Igl, Wood, The paper code

143. ISA-VAE: Independent subspace analysis with variational autoencoders. Anon. paper

144. Manifold mixup: better representations by interpolating hidden states. Verma, Lamb, Beckham, Najafi, Mitliagkas, Courville, Lopez-Paz, Bengio. paper code

145. Bit-swap: recursive bits-back coding for lossless compression with hierarchical latent variables. Kingma, Abbeel, Ho.paper code

146. Practical lossless compression with latent variables using bits back coding. Townsend, Bird, Barber. paper code

147. BIVA: a very deep hierarchy of latent variables for generative modeling. Maaloe, Fraccaro, Lievin, Winther. paper

148. Flow++: improving flow-based generative models with variational dequantization and architecture design. Ho, Chen, Srinivas, Duan, Abbeel. paper code

149. Sylvester normalizing flows for variational inference. van den Berg, Hasenclever, Tomczak, Welling. paper code

150. Unbiased implicit variational inference. Titsias, Ruiz. paper

151. Robustly disentangled causal mechanisms: validating deep representations for interventional robustness. Suter, Miladinovic, Scholkopf, Bauer. paper

152. Tutorial: Deriving the standard variational autoencoder (VAE) loss function. Odaibo paper

153. Learning disentangled representations with reference-based variational autoencoders. Ruiz, Martinez, Binefa, Verbeek. paper

154. Disentangling factors of variation using few labels. Locatello, Tschannen, Bauer, Ratsch, Scholkopf, Bachem paper

155. Disentangling disentanglement in variational autoencoders Mathieu, Rainforth, Siddharth, The, paper code

156. LIA: latently invertible autoencoder with adversarial learning Zhu, Zhao, Zhang paper

157. Emerging disentanglement in auto-encoder based unsupervised image content transfer. Press, Galanti, Benaim, Wolf paper code

158. MAE: Mutual posterior-divergence regularization for variational autoencoders Ma, Zhou, Hovy paper code

159. Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision. Lezama paper code code

160. Challenging common assumptions in the unsupervised learning of disentangled representations. Locatello, Bauer, Lucic, Ratsch, Gelly, Scholkopf, Bachem paper code

161. Variational prototyping encoder: one shot learning with prototypical images. Kim, Oh, Lee, Pan, Kweon paper

162. Diagnosing and enchanving VAE models (conf and journal paper both available). Dai, Wipf paper code

163. Disentangling latent hands for image synthesis and pose estimation. Yang, Yao paper

164. Rare event detection using disentangled representation learning. Hamaguchi, Sakurada, Nakamura paper

165. Disentangling latent space for VAE by label relevant/irrelvant dimensions. Zheng, Sun paper code

166. Variational autoencoders pursue PCA directions (by accident). Rolinek, Zietlow, Martius paper

167. Disentangled Representation learning for 3D face shape. Jiang, Wu, Chen, Zhang paper code

168. Preventing posterior collapse with delta-VAEs. Razavi, van den Oord, Poole, Vinyals paper code

169. Gait recognition via disentangled representation learning. Zhang, Tran, Yin, Atoum, Liu, Wan, Wang paper

170. Hierarchical disentanglement of discriminative latent features for zero-shot learning. Tong, Wang, Klinkigt, Kobayashi, Nonaka paper

171. Generalized zero- and few-shot learning via aligned variational autoencoders. Schonfeld, Ebrahimi, Sinha, Darrell, Akata paper code

172. Unsupervised part-based disentangling of object shape and appearance. Lorenz, Bereska, Milbich, Ommer paper

173. A semi-supervised Deep generative model for human body analysis. de Bem, Ghosh, Ajanthan, Miksik, Siddaharth, Torr paper

174. Multi-object representation learning with iterative variational inference. Greff, Kaufman, Kabra, Watters, Burgess, Zoran, Matthey, Botvinick, Lerchner paper code

175. Generating diverse high-fidelity images with VQ-VAE-2. Razavi, van den Oord, Vinyals paper code code

176. MONet: unsupervised scene decomposition and representation. Burgess, Matthey, Watters, Kabra, Higgins, Botvinick, Lerchner paper

177. Structured disentangled representations and Hierarchical disentangled representations. Esmaeili, Wu, Jain, Bozkurt, Siddarth, Paige, Brooks, Dy, van de Meent paper

178. Spatial Broadcast Decoder: A simple architecture for learning disentangled representations in VAEs. Watters, Matthey, Burgess, Lerchner paper code

179. Resampled priors for variational autoencoders. Bauer, Mnih paper

180. Weakly supervised disentanglement by pairwise similiarities. Chen, Batmanghelich paper

181. Deep variational information bottleneck. Aelmi, Fischer, Dillon, Murphy paper code

182. Generalized variational inference. Knoblauch, Jewson, Damoulas paper

183. Variational autoencoders and nonlinear ICA: a unifying framework. Khemakhem, Kingma paper

184. Lagging inference networks and posterior collapse in variational autoencoders. He, Spokoyny, Neubig, Berg-Kirkpatrick paper code

185. Avoiding latent variable collapse with generative skip models. Dieng, Kim, Rush, Blei paper

186. Distribution Matching in Variational inference. Rosca, Lakshminarayana, Mohamed paper A variational auto-encoder model for stochastic point process. Mehrasa, Jyothi, Durand, He, Sigal, Mori paper

187. Sliced-Wasserstein auto-encoders. Kolouri, Pope, Martin, Rohde paper code

188. A deep generative model for graph layout. Kwon, Ma paper

189. Differentiable perturb-and-parse semi-supervised parsing with a structured variational autoencoder. Corro, Titov paper code

190. Variational autoencoders with jointly optimized latent dependency structure. He, Gong, Marino, Mori, Lehrmann paper code

191. Unsupervised learning of spatiotemporally coherent metrics Goroshin, Bruna, Tompson, Eigen, LeCun paper

192. Temporal difference variational auto-encoder. Gregor, Papamakarios, Besse, Buesing, Weber paper code

193. Representation learning with contrastive predictive coding. van den Oord, Li, Vinyals paper code

194. Representation disentanglement for multi-task learning with application to fetal ultrasound Meng, Pawlowski, Rueckert, Kainz paper

195. M$2$VAE - derivation of a multi-modal variational autoencoder objective from the marginal joint log-likelihood. Korthals paper

196. Predicting visual memory schemas with variational autoencoders. Kyle-Davidson, Bors, Evans paper

197. T-CVAE: Transformer -based conditioned variational autoencoder for story completion. Wang, Wan paper code

198. PuVAE: A variational autoencoder to purify adversarial examples. Hwang, Park, Jang, Yoon, Cho paper

199. Coupled VAE: Improved accuracy and robustness of a variational autoencoder. Cao, Li, Nelson paper

200. D-VAE: A variational autoencoder for directed acyclic graphs. Zhang, Jiang, Cui, Garnett, Chen paper code

201. Are disentangled representations helpful for abstract reasoning? van Steenkiste, Locatello, Schmidhuber, Bachem paper

202. A heuristic for unsupervised model selection for variational disentangled representation learning. Duan, Watters, Matthey, Burgess, Lerchner, Higgins paper

203. Dual space learning with variational autoencoders. Okamoto, Suzuki, Higuchi, Ohsawa, Matsuo paper

204. Variational autoencoders for sparse and overdispersed discrete data. Zhao, Rai, Du, Buntine paper

205. Variational auto-decoder. Zadeh, Lim, Liang, Morency. paper

206. Causal discovery with attention-based convolutional neural networks. Naura, Bucur, Seifert paper

207. Variational laplace autoencoders. Park, Kim, Kim paper

208. Variational autoencoders with normalizing flow decoders. paper

209. Gaussian process priors for view-aware inference. Hou, Heljakka, Solin paper

210. SGVAE: sequential graph variational autoencoder. Jing, Chi, Tang paper

211. improving multimodal generative models with disentangled latent partitions. Daunhawer, Sutter, Vogt paper

212. Cross-population variational autoencoders. Davison, Severson, Ghosh paper paper

213. Evidential disambiguation of latent multimodality in conditional variational autoencoders. Itkina, Ivanovic, Senanayake, Kochenderfer, Pavone paper

214. Increasing the generalisation capacity of conditional VAEs. Klushyn, Chen, Cseke, Bayer, van der Smagt paper

215. Multi-source neural variational inference. Kurle, Gunnemann, van der Smagt paper

216. Early integration for movement modeling in latent spaces. Hornung, Chen, van der Smagt paper

217. Building face recognition system with triplet-based stacked variational denoising autoencoder. LEe, Hart, Richens, Johri paper

218. Cross-domain variational autoencoder for recommender systems. Shi, Wang paper Predictive coding, variational autoencoders, and biological connections. Marino paper

219. A general and adaptive robust loss function Barron paper

220. Variational autoencoder trajectory primitives and discrete latent. Osa, Ikemoto paper

221. Faster attend-infer-repeat with tractable probabilistic models. Stelzner, Peharz, Kersting paper code

222. Learning predictive models from observation and interaction. Schmeckpeper, Xie, Rybkin, Tian, Daniilidis, Levine, Finn paper

223. Translating visual art into music Muller-Eberstein, van Noord paper

224. Non-parallel voice conversion with controllable speaker individuality using variational autoencoder. Ho, Akagi paper

225. Derivation of the variational Bayes equations. Maren paper

2019 (with code)

1. Explicitly disentangling image content from translation and rotation with spatial-VAE Tristan W Bepler, Ellen Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger paper code

2. Diagnosing and Enhancing VAE Models. Bin Dai, David Wipf paper code

3. D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen paper code

4. Good Semi-supervised VAE Requires Tighter Evidence Lower Bound Haozhe Feng, Kezhi Kong, Tianye Zhang, Siyue Xue, Wei Chen paper code

5. An Information Theoretic Perspective on Disentangled Representation Learning Xiaojiang Yang, Wendong Bi, Yu Cheng, Junchi Yan paper code

6. Good Semi-supervised VAE Requires Tighter Evidence Lower Bound Haozhe Feng, Kezhi Kong, Tianye Zhang, Siyue Xue, Wei Chen paper code

7. Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN) Peter Sorrenson, Carsten Rother, Ullrich Köthe paper code

8. RTC-VAE: HARNESSING THE PECULIARITY OF TOTAL CORRELATION IN LEARNING DISENTANGLED REPRESENTATIONS Ze Cheng, Juncheng B Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze paper code

9. RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering Sam Lobel*, Chunyuan Li*, Jianfeng Gao, Lawrence Carin paper code

10. Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alan Aspuru-Guzik paper code

11. Explicitly disentangling image content from translation and rotation with spatial-VAE Tristan W Bepler, Ellen Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger paper code

12. Learning to Dress 3D People in Generative Clothing Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, Michael J. Black paper

13. Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi paper

14. Generating Diverse High-Resolution Images with VQ-VAE Ali Razavi, Aaron van den Oord, Oriol Vinyals paper

15. WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding. Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts paper

16. Diagnosing and Enhancing VAE Models. Bin Dai, David Wipf paper

17. VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation. Hsien-Yu Meng, Lin Gao, Yu-Kun Lai, Dinesh Manocha paper

18. Uncertainty Analysis of VAE-GANs for Compressive Medical Imaging Mardani, Joseph Cheng, Shreyas Vasanawala, John Pauly paper

19. Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions. Zhilin Zheng, Li Sun paper

20. Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Ladislav Rampášek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg paper

21. PQ-VAE:EfficientRecommendation UsingQuantizedEmbeddings Jan Van Balen, Mark Levy paper

22. Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic paper

23. Improving VAE generations of multimodal data through data-dependent conditional priors. Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis paper

24. D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen paper

25. Explicitly disentangling image content from translation and rotation with spatial-VAE Tristan Bepler, Ellen Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger [paper]( http://papers.nips.cc/

26. paper/9677-explicitly-disentangling-image-content-from-translation-and-rotation-with-spatial-vae.pdf )

27. Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior. Mario González, Andrés Almansa, Mauricio Delbracio, Pablo Musé, Pauline Tan paper

28. ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki paper

29. Relevance Factor VAE: Learning and Identifying Disentangled Factors Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic paper

30. Joint haze image synthesis and dehazing with mmd-vae losses Zongliang Li, Chi Zhang, Gaofeng Meng, Yuehu Liu paper

31. Semi-supervised Open Domain Information Extraction with Conditional VAE Zhengbao Jiang (zhengbaj), Songwei Ge (songweig), Ruohong Zhang (ruohongz), Donghan Yu (dyu2)paper

32. Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction. Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han paper

33. Sequential VAE-LSTM for Anomaly Detection on Time Series. Run-Qing Chen, Guang-Hui Shi, Wan-Lei Zhao, Chang-Hui Liang paper

34. Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE. Chao Ma, Wenbo Gong, SebastianTschiatschek2 Sebastian Tschiatschek, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang paper

35. Increasing Expressivity of a Hyperspherical VAE Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves paper

36. VAE-based regularization for deep speaker embedding. Yang Zhang, Lantian Li, Dong Wang paper

37. G-VAE: A Continuously Variable Rate Deep Image Compression Framework. Ze Cui, Jing Wang, Bo Bai, Tiansheng Guo, Yihui Feng paper

38. Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction Zhizhong Han, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker paper

39. Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder. Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon paper

40. The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. Bin Dai, Ziyu Wang, David Wipf paper

41. Disentangling the Spatial Structure and Style in Conditional VAE. Ziye Zhang, Li Sun, Zhilin Zheng, Qingli Li paper

42. Inspecting and Interacting with Meaningful Music Representations using VAE. Ruihan Yang, Tianyao Chen, Yiyi Zhang, Gus Xia paper

43. An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE. Tianye Zhang, Haozhe Feng, Zexian Chen, Can Wang, Yanhao Huang, Yong Tang, Wei Chen paper

44. Class-Conditional VAE-GAN for Local-Ancestry Simulation. Daniel Mas Montserrat, Carlos Bustamante, Alexander Ioannidis paper

45. VAE-based Domain Adaptation for Speaker Verification. Xueyi Wang, Lantian Li, Dong Wang paper

46. Constructing the Matrix Multilayer Perceptron and its Application to the VAE. Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön paper

47. Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization. Cedric De Boom, Samuel Wauthier, Tim Verbelen, Bart Dhoedt paper

48. MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation. Xia Liang, Junmin Wu, Jing Cao paper

49. Program Synthesis and Vulnerability Injection Using a Grammar VAE Leonard Kosta, Laura Seaman, Hongwei Xi paper

50. SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations Chen Wang, Chengyuan Deng, Vladimir Ivanov paper

51. A Closer Look at Disentangling in β-VAE. Harshvardhan Sikka, Weishun Zhong, Jun Yin, Cengiz Pehlevan paper

52. Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement. Mostafa Sadeghi, Xavier Alameda-Pineda paper

53. CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks. Jianlin Liu, Fenxiong Chen,Jun Yan and Dianhong Wang paper

54. Generated Loss and Augmented Training of MNIST VAE. Jason Chou paper

55. Learning Facial Recognition Biases through VAE Latent Representations. Diego Celis, Meghana Rao paper

56. The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels Michael Pearce paper

57. Class-Conditional VAE-GAN for Local-Ancestry Simulation. Daniel Mas Montserrat, Carlos Bustamante, Alexander Ioannidis paper

58. Investigating GAN and VAE to Train DCNN Soundararajan Ezekiel, Larry Pearlstein, Abdullah Ali Alshehri, Adam Lutz, Jackson Zaunegger, and Waleed Farag paper

59. MIDI-Sandwich: Multi-model Multi-task Hierarchical Conditional VAE-GAN networks for Symbolic Single-track Music Generation. Xia Liang, Junmin Wu, Yan Yin paper

60. Generated Loss, Augmented Training, and Multiscale VAE. Jason Chou, Gautam Hathi paper

61. Based on Graph-VAE Model to Predict Student's Score. Yang Zhang, Mingming Lu paper

62. Graph Embedding VAE: A Permutation Invariant Model of Graph Structure. Tony Duan, Juho Lee paper

63. Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning. J. Jon Ryu, Yoojin Choi, Young-Han Kim, Mostafa El-Khamy, Jungwon Lee paper

64. VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization. Hyungtak Choi, Lohith Ravuru, Tomasz Dryjanski, Seonghan Ryu, Donghyun Lee, Hojung Lee and Inchu Hwang paper

65. Effect of VAE Latex Powder Addition on Tensile and Shear Properties of Styrene-Acrylate Based Cement Composite Joint Compound Meng Boxu, Xu Jinyu, Gu Chao and Peng guangpaper

66. BooVAE: A scalable framework for continual VAE learning under boosting approach Anna Kuzina, Evgenii Egorov, Evgeny Burnaev paper

67. ρ-VAE: Autoregressive parametrization of the VAE encoder. Sohrab Ferdowsi, Maurits Diephuis, Shideh Rezaeifar, Slava Voloshynovskiy paper

68. M2VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood. Timo Korthals paper

69. Progressive VAE Training on Highly Sparse and Imbalanced Data. Dmitry Utyamishev, Inna Partin-Vaisband paper

70. retina-VAE: Variationally Decoding the Spectrum of Macular Disease. Stephen G. Odaibo paper

71. GP-VAE: Deep Probabilistic Time Series Imputation. Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt paper

72. Latent Space Expanded Variational Autoencoder for Sentence Generation. TIANBAO SONG , JINGBO SUN, BO CHEN, WEIMING PENG, AND JIHUA SONG paper

73. RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. Ilya Shenbin, Anton Alekseev, Elena Tutubalina paper

74. Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models. Lennart Brocki, Neo Christopher Chung paper

75. MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation Xia Liang, Junmin Wu and Jing Cao paper

76. SCALABLE MODELING OF SPATIOTEMPORAL DATA USING THE VARIATIONAL AUTOENCODER: AN APPLICATION IN GLAUCOMA By Samuel I. Berchuck, Felipe A. Medeiros and Sayan Mukherjee paper

77. Learning Discrete and Continuous Factors of Data via Alternating Disentanglement Yeonwoo Jeong, Hyun Oh Song paper

78. Variational Adversarial Active Learning Samarth Sinha, Sayna Ebrahimi, Trevor Darrell paper

79. Using RGB Image as Visual Input for Mapless Robot Navigation Liulong Ma, Yanjie Liu and Jiao Chen paper

80. Diagnosing and Enhancing VAE Models. Bin Dai, David Wipf paper

81. PRACTICAL LOSSLESS COMPRESSION WITH LATENT VARIABLES USING BITS BACK CODING James Townsend, Thomas Bird & David Barber paper

82. Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks. Xiang Li, and Shihao Ji paper

2020

1. GOOD SEMI-SUPERVISED VAE REQUIRES TIGHTER EVIDENCE LOWER BOUND Haozhe Feng, Kezhi Kong, Tianye Zhang, Siyue Xue, Wei Chen paper code

2. Learning Discrete and Continuous Factors of Data via Alternating Disentanglement YeonwooJeong, HyunOhSong paper code supportingDocs

3. D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chenpaper code

4. q-VAE for Disentangled Representation Learning and Latent Dynamical Systems. Taisuke Kobayashi paper

5. Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE. Vijaya Kumar, Sundar, Shreyas Ramakrishna, Zahra Rahiminasab, Arvind Easwaran, Abhishek Dubey paper

6. Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement. Junxiang Chen, Kayhan Batmanghelich paper

7. Deterministic Decoding for Discrete Data in Variational Autoencoders. Daniil Polykovskiy, Dmitry Vetrov paper

8. Dimensionality Reduction of SDSS Spectra with Variational Autoencoders. Stephen K. N. Portillo, John K. Parejko, Jorge R. Vergara, and Andrew J. Connolly paper

9. NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Aggregated Convolutional Feature Maps. Maximilian Seitzer paper

10. An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection. Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat, Nick Pawlowski, Poramate Manoonpong, and Nat Dilokthanakul paper

11. Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation Chaochao Yan, Sheng Wang, Jinyu Yang, Tingyang Xu, Junzhou Huang paper

12. RACT: TOWARDS AMORTIZED RANKING-CRITICAL TRAINING FOR COLLABORATIVE FILTERING Sam Lobel, Chunyuan Li, Jianfeng Gao, Lawrence Carin paper

13. Draft and Edit: Automatic Storytelling Through Multi-Pass Hierarchical Conditional Variational Autoencoder. Yu, Li, Liu, Tang, Zhang, Zhao, Yanpaper

14. Reverse variational autoencoder for visual attribute manipulation and anomaly detection. Gauerhof, Gu paper

15. Bridged variational autoencoders for joint modeling of images and attributes. Yadav, Sarana, Namboodiri, Hegde paper

16. Treatment effect estimation with disentangled latent factors. anon paper

17. Unbalanced GANS: pre-training the generator of generative adversarial network using variational autoencoder. Ham, Jun, Kim paper

18. Regularized autoencoders via relaxed injetive probability flow. Kumar, Poole, Murphy paper

19. Out-of-distribution detection with distance guarantee in deep generative models. Zhang, Liu, Chen, Wang, Liu, Li, Wei, Chen paper

20. Balancing reconstruction error and Kullback-Leibler divergence in variational autoencoders. Asperti, Trentin paper

21. Data augmentation for historical documents via cascade variational auto-encoder. Cao, Kamata paper

22. Controlling generative models with continuous factors of variations. Plumerault, Borgne, Hudelot paper

23. Towards a controllable disentanglement network. Song, Koyejo, Zhang paper

24. Knowledge-induced learning with adaptive sampling variational autoencoders for open set fault diagnostics. Chao, Adey, Fink paper

25. NestedVAE: isolating common factors via weak supervision. Vowels, Camgoz, Bowden paper

26. Leveraging cross feedback of user and item embeddings for variational autoencoder based collaborative filtering. Jin, Zhao, Du, Liu, Gao, Li, Xu paper

27. K-autoencoders deep clustering. Opochinsky, Chazan, Gannot, Goldberger paper

28. D2D-TM: a cycle VAE-GAN for multi-domain collaborative filtering. Nguyen, Ishigaki paper

29. Disentangling controllable object through video prediction improves visual reinforcement learning. Zhong, Schwing, Peng paper

30. A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis. Lin, Mukherjee, Kannan paper

31. Context conditional variational autoencoder for predicting multi-path trajectories in mixed traffic. Cheng, Liao, Yang, Sester, Rosenhahn paper

32. Optimizing variational graph autoencoder for community detection with dual optimization. Choong, Liu, Murata

33. Learning flat latent manifolds with VAEs. Chen, Klushyn, Ferroni, Bayer, van der Smagt paper

34. Learning discrete distributions by dequantization. Hoogeboom, Cohen, Tomczak paper

35. Learning discrete and continuous factors of data via alternating disentanglement. Jeong, Song [paper](http://proceedings.mlr.press/v97/jeong19d/jeong19d.pdf https://github.com/snu-mllab/DisentanglementICML19)

36. Electrocardiogram generation and feature extraction using a variational autoencoder. Kuznetsov, Moskalenko, Zolotykh paper

37. CosmoVAE: variational autoencoder for CMB image inpainting. Yi, Guo, Fan, Hamann, Wang paper

38. Unsupervised representation disentanglement using cross domain features and adversarial learning in variational autoencoder based voice conversion. Huang, Luo, Hwang, Lo, Peng, Tsao, Wang paper

39. On implicit regularization in beta VAEs. Kumar, Poole paper

40. Weakly-supervised disentanglement without compromises. Locatello, Poole, Ratsch, Scholkopf, Bachem, Tschannen paper

41. An integrated framework based on latent variational autoencoder for providing early warning of at-risk students. Du, Yang, Hung paper

42. Variational autoencoder and friends. Zheng paper

43. High-fidelity synthesis with disentangled representation. Lee, Kim, Hong, Lee paper

44. Neurosymbolic knowledge representation for explainable and trustworthy AI. Malo paper

45. Adversarial disentanglement with grouped observations. Nemeth paper

46. AE-OT-GAN: Training GANs from data specific latent distribution. An, Guo, Zhang, Qi, Lei, Yau, Gu paper

47. AE-OT: a new generative model based on extended semi-discrete optimal transport. An, Guo, Lei, Luo, Yau, Gu paper

48. Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN). Sorrenson, Rother, Kothe paper

49. Phase transitions for the information bottleneck in representation learning. Wu, Fischer paper

50. Bayesian deep learning: a model-based interpretable approach. Matsubara paper

51. SPACE: unsupervised object-oriented scene representation via spatial attention and decomposition. Lin, Wu, Peri, Sun, Singh, Deng, Jiang, Ahn paper

52. A variational stacked autoencoder with harmony search optimizer for valve train fault diagnosis of diesel engine. Chen, Mao, Zhao, Jiang, Zhang paper

53. Evaluating loss compression rates of deep generative models. anon paper

54. Progressive learning and disentanglement of hierarchical representations. anon paper

55. Learning group structure and disentangled representations of dynamical environments. Quessard, Barrett, Clements paper

56. A simple framework for contrastive learning of visual representations. Chen, Kornblith, Norouzi, Hinton paper

57. WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding. Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts paper

58. Learning to Dress 3D People in Generative Clothing. Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, Michael J. Black paper

59. VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation. Hsien-Yu Meng, Lin Gao, Yu-Kun Lai, Dinesh Manocha paper

2019

2018

1. DVAE++: Discrete variational autoencoders wth overlapping transformations. Vahdat, Macready, Bian,Khoshaman, Andriyash paper

2. FFJORD: free-form continuous dynamics for scalable reversible generative models. Grathwohl, Chen, Bettencourt, Sutskever, Duvenaud paper

3. A general method for amortizing variational filtering. Marino, Cvitkovic, Yue paper code

4. Handling incomplete heterogeneous data using VAEs. Nazabal, Olmos, Ghahramani, Valera paper

5. Sequential attend, infer, repeat: generative modeling of moving objects. Kosiorek, Kim, Posner, Teh paper code youtube

6. Doubly reparameterized gradient estimators for monte carlo objectives. Tucker, Lawson, Gu, Maddison paper

7. Interpretable intuitive physics model. Ye, Wang, Davidson, Gupta paper code

8. Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows. Eric Jang paper

9. Neural autoregressive flows. Huang, Krueger, Lacoste, Courville paper link code

10. Gaussian process prior variational autoencoders. Casale, Dalca, Sagletti, Listgarten, Fusi paper

11. ACVAE-VC: non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder. Kameoka, Kaneko, Tanaka, Hojo paper

12. Discovering interpretable representations for both deep generative and discriminative models. Adel, Ghahramani, Weller paper

13. Autoregressive quantile networks for generative modelling . Ostrovski, Dabey, Munos paper

14. Probabilistic video generation using holistic attribute control. He, Lehrmann, Marino, Mori, Sigal paper

15. Bias and generalization in deep generative models: an empirical study. Zhao, Ren, Yuan, Song, Goodman, Ermon paper link code

16. On variational lower bounds of mutual information. Poole, Ozair, van den Oord, Alemi, Tucker paper

17. GAN - why it is so hard to train generative adversarial networks . Hui paper

18. Counterfactuals uncover the modular structure of deep generative models. Besserve, Sun, Scholkopf. paper

19. Learning independent causal mechanisms. Parascandolo, Kilbertus, Rojas-Carulla, Scholkopf paper

20. Emergence of invariance and disentanglement in deep representations. Achille, Soatto paper

21. Variational memory encoder-decoder. Le, Tran, Nguyen, Venkatesh paper code

22. Variational autoencoders for collaborative filtering. Liang, Krishnan, Hoffman, Jebara paper

23. Invariant representations without adversarial training. Moyer, Gao, Brekelmans, Steeg, Galstyan paper code

24. Density estimation: Variational autoencoders. Rui Shu paper

25. TherML: The thermodynamics of machine learning. Alemi, Fishcer paper

26. Leveraging the exact likelihood of deep latent variable models. Mattei, Frellsen paper

27. What is wrong with VAEs? Kosiorek paper

28. Stochastic variational video prediction. Babaeizadeh, Finn, Erhan, Campbell, Levine paper code

29. Variational attention for sequence-to-sequence models. Bahuleyan, Mou, Vechtomova, Poupart paper code

30. FactorVAE Disentangling by factorizing. Kim, Minh paper

31. Disentangling factors of variation with cycle-consistent variational autoencoders. Jha, Anand, Singh, Veeravasarapu paper code

32. Isolating sources of disentanglement in VAEs. Chen, Li, Grosse, Duvenaud paper

33. VAE with a VampPrior. Tomczak, Welling paper

34. A Framework for the quantitative evaluation of disentangled representations. Eastwood, Williams paper code

35. Recent advances in autoencoder based representation learning. Tschannen, Bachem, Lucic paper

36. InfoVAE: Balancing learning and inference in variational autoencoders. Zhao, Song, Ermon paper

37. Understanding disentangling in Beta-VAE. Burgess, Higgins, Pal, Matthey, Watters, Desjardins, Lerchner paper

38. Hidden Talents of the Variational autoencoder. Dai, Wang, Aston, Hua, Wipf paper

39. Variational Inference of disentangled latent concepts from unlabeled observations. Kumar, Sattigeri, Balakrishnan paper

40. Self-supervised learning of a facial attribute embedding from video. Wiles, Koepke, Zisserman paper

41. Wasserstein auto-encoders. Tolstikhin, Bousquet, Gelly, Scholkopf paper

42. A two-step disentanglement. method Hadad, Wolf, Shahar paper code

43. Taming VAEs. Rezende, Viola paper code code

44. IntroVAE Introspective variational autoencoders for photographic image synthesis. Huang, Li, He, Sun, Tan paper code

45. Information constraints on auto-encoding variational bayes. Lopez, Regier, Jordan, Yosef paper code

46. Learning disentangled joint continuous and discrete representations. Dupont paper code

47. Neural discrete representation learning. van den Oord, Vinyals, Kavukcuoglu paper code code

48. Disentangled sequential autoencoder. Li, Mandt paper code

49. Variational Inference: A review for statisticians. Blei, Kucukelbir, McAuliffe paper Advances in Variational Inferece. Zhang, Kjellstrom paper

50. Auto-encoding total correlation explanation. Goa, Brekelmans, Steeg, Galstyan paper Closest: code

51. Fixing a broken ELBO. Alemi, Poole, Fischer, Dillon, Saurous, Murphy paper

52. The information autoencoding family: a lagrangian perspective on latent variable generative models. Zhao, Song, Ermon paper code

53. Debiasing evidence approximations: on importance-weighted autoencoders and jackknife variational inference. Nowozin paper code

54. Unsupervised discrete sentence representation learning for interpretable neural dialog generation. Zhao, Lee, Eskenazi paper paper code

55. Dual swap disentangling. Feng, Wang, Ke, Zeng, Tao, Song paper

56. Multimodal generative models for scalable weakly-supervised learning. Wu, Goodman paper code code

57. Do deep generative models know what they don't know? Nalisnick, Matsukawa, The, Gorur, Lakshminarayanan paper

58. Glow: generative flow with invertible 1x1 convolutions. Kingma, Dhariwal paper code code

59. Inference suboptimality in variational autoencoders. Cremer, Li, Duvenaud paper code

60. Adversarial Variational Bayes: unifying variational autoencoders and generative adversarial networks. Mescheder, Mowozin, Geiger paper code

61. Semi-amortized variational autoencoders. Kim, Wiseman, Miller, Sontag, Rush paper code

62. Spherical Latent Spaces for stable variational autoencoders. Xu, Durrett paper code

63. Hyperspherical variational auto-encoders. Davidson, Falorsi, De Cao, Kipf, Tomczak paper code code

64. Fader networks: manipulating images by sliding attributes. Lample, Zeghidour, Usunier, Bordes, Denoyer, Ranzato paper code

65. Training VAEs under structured residuals. Dorta, Vicente, Agapito, Campbell, Prince, Simpson paper code

66. oi-VAE: output interpretable VAEs for nonlinear group factor analysis. Ainsworth, Foti, Lee, Fox paper code

67. infoCatVAE: representation learning with categorical variational autoencoders. Lelarge, Pineau paper code

68. Iterative Amortized inference. Marino, Yue, Mandt paper paper code

69. On unifying Deep Generative Models. Hu, Yang, Salakhutdinov, Xing paper

70. Diverse Image-to-image translation via disentangled representations. Lee, Tseng, Huang, Singh, Yang paper code

71. PIONEER networks: progressively growing generative autoencoder. Heljakka, Solin, Kannala paper code

72. Towards a definition of disentangled representations. Higgins, Amos, Pfau, Racaniere, Matthey, Rezende, Lerchner paper

73. Life-long disentangled representation learning with cross-domain latent homologies. Achille, Eccles, Matthey, Burgess, Watters, Lerchner, Higgins paper

74. Learning deep disentangled embeddings with F-statistic loss. Ridgeway, Mozer paper code

75. Learning latent subspaces in variational autoencoders. Klys, Snell, Zemel paper

76. On the latent space of Wasserstein auto-encoders. Rubenstein, Scholkopf, Tolstikhin. paper code

77. Learning disentangled representations with Wasserstein auto-encoders. Rubenstein, Scholkopf, Tolstikhin paper

78. The mutual autoencoder: controlling information in latent code representations. Phuong, Kushman, Nowozin, Tomioka, Welling paper paper paper

79. Auxiliary guided autoregressive variational autoencoders. Lucas, Verkbeek paper code

80. Interventional robustness of deep latent variable models. Suter, Miladinovic, Bauer, Scholkopf paper

81. Understanding degeneracies and ambiguities in attribute transfer. Szabo, Hu, Portenier, Zwicker, Facaro paper DNA-GAN: learning disentangled representations from multi-attribute images. Xiao, Hong, Ma [paper](https://arxiv.org/pdf/1711.05415.pdf https://github.com/Prinsphield/DNA-GAN)

82. Normalizing flows. Kosiorek paper

83. Hamiltonian variational auto-encoder Caterini, Doucet, Sejdinovic paper

84. Causal generative neural networks. Goudet, Kalainathan, Caillou, Guyon, Lopez-Paz, Sebag. paper code

85. Flow-GAN: Combining maximum likelihood and adversarial learning in generative models. Grover, Dhar, Ermon paper code

86. Linked causal variational autoencoder for inferring paired spillover effects. Rakesh, Guo, Moraffah, Agarwal, Liu paper code

87. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. Xu, Chen, Zhao, Li, Bu, Li, Liu, Zhao, Pei, Feng, Chen, Wang, Qiao paper

88. Mutual information neural estimation. Belghazi, Baratin, Rajeswar, Ozair, Bengio, Hjelm. paper code code

89. Explorations in homeomorphic variational auto-encoding. Falorsi, de Haan, Davidson, Cao, Weiler, Forre, Cohen. paper code

90. Hierarchical variational memory network for dialogue generation. Chen, Ren, Tang, Zhao, Yin paper

91. World models. Ha, Schmidhuber paper

2017

1. The concrete distribution: a continuous relaxation of discrete random variables. Maddison, Mnih, The paper

2. Categorical reparameterization with Gumbel-Softmax. Jang, Gu, Poole paper

3. Opening the black box of deep neural networks via information. Schwartz-Ziv, Tishby paper youtube

4. Discovering causal signals in images . Lopez-Paz, Nishihara, Chintala, Scholkopf, Bottou paper

5. Autoencoding variational inference for topic models. Srivastava, Sutton paper

6. Hidden Markov model variational autoencoder for acoustic unit discovery. Ebbers, Heymann, Drude, Glarner, Haeb-Umbach, Raj paper

7. Application of variational autoencoders for aircraft turbomachinery design. Zalger paper

8. Semi-supervised learning with variational autoencoders. Keng paper

9. Causal effect inference with deep latent variable models. Louizos, Shalit, Mooij, Sontag, Zemel, Welling paper code

10. beta-VAE: learning basic visual concepts with a constrained variational framework. Higgins, Matthey, Pal, Burgess, Glorot, Botvinick, Mohamed, Lerchner paper

11. Challenges in disentangling independent factors of variation. Szabo, Hu, Portenier, Facaro, Zwicker paper code

12. Composing graphical models with neural networks for structured representations and fast inference. Johnson, Duvenaud, Wiltschko, Datta, Adams paper

13. Split-brain autoencoders: unsupervised learning by cross-channel prediction. Zhang, Isola, Efros paper

14. Learning disentangled representations with semi-supervised deep generative models.Siddharth, Paige, van de Meent, Desmaison, Goodman, Kohli, Wood, Torr paper code

15. Learning hierarchical features from generative models. Zhao, Song, Ermon paper code

16. Multi-level variational autoencoder: learning disentangled representations from grouped observations. Bouchacourt, Tomioka, Nowozin paper

17. Neural Face editing with intrinsic image disentangling. Shu, Yumer, Hadap, Sankavalli, Shechtman, Samaras paper code

18. Variational Lossy Autoencoder. Chen, Kingma, Salimans, Duan, Dhariwal, Schulman, Sutskever, Abbeel paper code

19. Unsupervised learning of disentangled and interpretable representations from sequential data. Hsu, Zhang, Glass paper code code

20. Factorized variational autoencoder for modeling audience reactions to movies. Deng, Navarathna, Carr, Mandt, Yue, Matthews, Mori paper

21. Learning latent representations for speech generation and transformation. Hsu, Zhang, Glass paper code

22. Unsupervised learning of disentangled representations from video. Denton, Birodkar paper code

23. Laplacian pyramid of conditional variational autoencoders. Dorta, Vicente, Agapito, Campbell, Prince, Simpson paper

24. Neural Photo Editing with Inrospective Adverarial Networks. Brock, Lim, Ritchie, Weston paper code

25. Discrete Variational Autoencoder. Rolfe paper code

26. Reinterpreting importance-weighted autoencoders. Cremer, Morris, Duvenaud paper code

27. Density Estimation using realNVP. Dinh, Sohl-Dickstein, Bengio paper code code

28. JADE: Joint autoencoders for disentanglement. Banijamali, Karimi, Wong, Ghosi paper Joint Multimodal learning with deep generative models. Suzuki, Nakayama, Matsuo paper code

29. Towards a deeper understanding of variational autoencoding models. Zhao, Song, Ermon paper code

30. Lagging inference networks and posterior collapse in variational autoencoders. Dilokthanakul, Mediano, Garnelo, Lee, Salimbeni, Arulkumaran, Shanahan paper code code

31. On the challenges of learning with inference networks on sparse, high-dimensional data. Krishnan, Liang, Hoffman paper code

32. Stick-breaking Variational Autoencoder. paper code

33. Deep variational canonical correlation analysis. Wang, Yan, Lee, Livescu paper code

34. Nonparametric variational auto-encoders for hierarchical representation learning. Goyal, Hu, Liang, Wang, Xing paper code

35. PixelSNAIL: An improved autoregressive generative model. Chen, Mishra, Rohaninejad, Abbeel paper code

36. Improved Variational Inference with inverse autoregressive flows. Kingma, Salimans, Jozefowicz, Chen, Sutskever, Welling paper code

37. It takes (only) two: adversarial generator-encoder networks. Ulyanov, Vedaldi, Lempitsky paper code

38. Symmetric Variational Autoencoder and connections to adversarial learning. Chen, Dai, Pu, Li, Su, Carin paper

39. Reconstruction-based disentanglement for pose-invariant face recognition. Peng, Yu, Sohn, Metaxas, Chandraker paper code

40. Is maximum likelihood useful for representation learning? Huszár paper

41. Disentangled representation learning GAN for pose-invariant face recognition. Tran, Yin, Liu paper code

42. Improved Variational Autoencoders for text modeling using dilated convolutions. Yang, Hu, Salakhutdinov, Berg-kirkpatrick paper

43. Improving variational auto-encoders using householder flow. Tomczak, Welling paper code

44. Sticking the landing: simple, lower-variance gradient estimators for variational inference. Roeder, Wu, Duvenaud. paper code

45. VEEGAN: Reducing mode collapse in GANs using implicit variational learning. Srivastava, Valkov, Russell, Gutmann. paper code

46. Discovering discrete latent topics with neural variational inference. Miao, Grefenstette, Blunsom paper

47. Variational approaches for auto-encoding generative adversarial networks. Rosca, Lakshminarayana, Warde-Farley, Mohamed paper

48. Variational Autoencoder and extensions. Courville paper

49. A neural representation of sketch drawings. Ha, Eck paper

2016

1. Attend, infer, repeat: fast scene understanding with generative models. Eslami, Heess, Weber, Tassa, Szepesvari, Kavukcuoglu, Hinton paper paper code

2. Deep feature consistent variational autoencoder. Hou, Shen, Sun, Qiu paper code

3. Neural variational inference for text processing. Miao, Yu, Grefenstette, Blunsom. paper

4. Domain-adversarial training of neural networks. Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette, Marchand, Lempitsky paper

5. Tutorial on Variational Autoencoders. Doersch paper

6. How to train deep variational autoencoders and probabilistic ladder networks. Sonderby, Raiko, Maaloe, Sonderby, Winther paper

7. ELBO surgery: yet another way to carve up the variational evidence lower bound. Hoffman, Johnson paper

8. Variational inference with normalizing flows. Rezende, Mohamed paper

9. The Variational Fair Autoencoder. Louizos, Swersky, Li, Welling, Zemel paper code

10. Information dropout: learning optimal representations through noisy computations. Achille, Soatto paper

11. Domain separation networks. Bousmalis, Trigeorgis, Silberman, Krishnan, Erhan paper code code

12. Disentangling factors of variation in deep representations using adversarial training. Mathieu, Zhao, Sprechmann, Ramesh, LeCunn paper code

13. Variational autoencoder for semi-supervised text classification. Xu, Sun, Deng, Tan paper code related: code

14. Learning what and where to draw. Reed, Sohn, Zhang, Lee paper

15. Attribute2Image: Conditional image generation from visual attributes. Yan, Yang, Sohn, Lee paper

16. Variational inference with normalizing flows. Rezende, Mohamed paper code

17. Wild Variational Approximations. Li, Liu paper

18. Importance Weighted Autoencoders. Burda, Grosse, Salakhutdinov paper code code code

19. Stacked What-Where Auto-encoders. Zhao, Mathieu, Goroshin, LeCunn paper code

20. Disentangling nonlinear perceptual embeddings with multi-query triplet networks. Veit, Belongie, Karaletsos paper

21. Ladder Variational Autoencoders. Sonderby, Raiko, Maaloe, Sonderby, Winther paper
    Variational autoencoder for deep learning of images, labels and captions. Pu, Gan Henao, Yuan, Li, Stevens, Carin paper

22. Approximate inference for deep latent Gaussian mixtures. Nalisnick, Hertel, Smyth paper code

23. Auxiliary Deep Generative Models. Maaloe, Sonderby, Sonderby, Winther paper code

24. Variational methods for conditional multimodal deep learning. Pandey, Dukkipati paper

25. PixelVAE: a latent variable model for natural images. Gulrajani, Kumar, Ahmed, Taiga, Visin, Vazquez, Courville paper code code

26. Adversarial autoencoders. Makhzani, Shlens, Jaitly, Goodfellow, Frey paper code

27. A hierarchical latent variable encoder-decoder model for generating dialogues. Serban, Sordoni, Lowe, Charlin, Pineau, Courville, Bengio paper

28. Infinite variational autoencoder for semi-supervised learning. Abbasnejad, Dick paper

29. f-GAN: Training generative neural samplers using variational divergence minimization. Nowozin, Cseke paper code

30. DISCO Nets: DISsimilarity Coefficient networks Bouchacourt, Kumar, Nowozin paper code

31. Information dropout: learning optimal representations through noisy computations. Achille, Soatto paper

32. Weakly-supervised disentangling with recurrent transformations for 3D view synthesis. Yang, Reed, Yang, Lee paper code

33. Autoencoding beyond pixels using a learned similarity metric. Boesen, Larsen, Sonderby, Larochelle, Winther paper code

34. Generating images with perceptual similarity metrics based on deep networks Dosovitskiy, Brox. paper code

35. A note on the evaluation of generative models. Theis, van den Oord, Bethge. paper

36. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. Chen, Duan, Houthooft, Schulman, Sutskever, Abbeel paper code

37. Disentangled representations in neural models. Whitney paper

38. A recurrent latent variable model for sequential data. Chung, Kastner, Dinh, Goel, Courville, Bengio paper

39. Unsupervised learning of 3D structure from images. Rezende, Eslami, Mohamed, Battaglia, Jaderberg, Heess paper

40. A survey of inductive biases for factorial representation-learning. Ridgeway paper

2015

1. Deep learning and the information bottleneck principle Tishby, Zaslavsky paper

2. Training generative neural networks via Maximum Mean Discrepancy optimization. Dziugaite, Roy, Ghahramani paper

3. NICE: non-linear independent components estimation. Dinh, Krueger, Bengio paper

4. Deep convolutional inverse graphics network. Kulkarni, Whitney, Kohli, Tenenbaum paper code

5. Learning structured output representation using deep conditional generative models. Sohn, Yan, Lee paper code

6. Latent variable model with diversity-inducing mutual angular regularization. Xie, Deng, Xing paper

7. DRAW: a recurrent neural network for image generation. Gregor, Danihelka, Graves, Rezende, Wierstra. paper code

8. Variational Inference II. Xing, Zheng, Hu, Deng paper

2014

1. Auto-encoding variational Bayes. Kingma, Welling paper

2. Learning to disentangle factors of variation with manifold interaction. Reed, Sohn, Zhang, Lee paper

3. Semi-supervised learning with deep generative models. Kingma, Rezende, Mohamed, Welling paper code code

4. Stochastic backpropagation and approximate inference in deep generative models. Rezende, Mohamed, Wierstra paper code

5. Representation learning: a review and new perspectives. Bengio, Courville, Vincent paper

2011

1. Transforming Auto-encoders. Hinton, Krizhevsky, Wang paper

2008

1. Graphical models, exponential families, and variational inference. Wainwright, Jordan et al

2004

1. Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. Honkela, Valpola paper

2000

1. The information bottleneck method. Tishby, Pereira, Bialek paper