Accepted Papers

  1. Yifeng Li and Xiaodan Zhu, Exploring Helmholtz Machine and Deep Belief Net in the Exponential Family Perspective
  2. Biswa Sengupta and Karl Friston, How Robust are Deep Neural Networks?
  3. Alex Graves, Jacob Menick and Aaron van den Oord, Associative Compression Networks for Representation Learning
  4. Amartya Sanyal, Varun Kanade and Philip Torr, Intriguing Properties of Learned Representations
  5. Giovanni Mariani, Florian Scheldegger, Roxana Istrate, Costas Bekas and Cristiano Malossi, BAGAN: Data Augmentation with Balancing GAN
  6. Guokun Lai, Bohan Li, Guoqing Zheng and Yiming Yang, Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data
  7. Emilien Dupont, Tuanfeng Zhang, Peter Tilke, Lin Liang and William Bailey, Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
  8. Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao and Stefano Ermon, Learning Controllable Fair Representations via Latent Variable Generative Models
  9. Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly and Tomasz Trzcinski, BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
  10. Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman and Stefano Ermon, Bias and Generalization in Deep Generative Models: An Empirical Study
  11. Bidisha Samanta, Abir De, Niloy Ganguly and Manuel Gomez-Rodriguez, Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design
  12. Michael Tschannen, Eirikur Agustsson and Mario Lucic, Deep Generative Models for Distribution-Preserving Lossy Compression
  13. Arash Mehrjou and Bernhard Schölkopf, Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems
  14. Akifumi Okuno and Hidetoshi Shimodaira, On representation power of neural network-based graph embedding and beyond
  15. Shengjia Zhao, Jiaming Song and Stefano Ermon, Lagrangian VAE: Dual Optimization for Latent Variable Generative Models
  16. Nhat Ho, Tan Nguyen, Ankit Patel, Anima Anandkumar, Michael Jordan and Richard Baraniuk, The Latent-Dependent Deep Rendering Model
  17. Kun Xu, Haoyu Liang, Jun Zhu and Bo Zhang, Deep Structured Generative Models
  18. Nicola De Cao and Thomas Kipf, MolGAN: An implicit generative model for small molecular graphs
  19. Bin Dai, Yu Wang, John Aston, Gang Hua and David Wipf, Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models
  20. Alexander Alemi and Ian Fischer, TherML: Thermodynamics of Machine Learning
  21. Joseph Marino, Milan Cvitkovic and Yisong Yue, A General Framework for Amortizing Variational Filtering
  22. Shane Barratt and Rishi Sharma, A Note on the Inception Score
  23. Ramiro Camino, Christian Hammerschmidt and Radu State, Generating Multi-Categorical Samples with Generative Adversarial Networks
  24. Shengjia Zhao, Jiaming Song and Stefano Ermon, InfoVAE: Balancing Learning and Inference in Variational Autoencoders
  25. Stanislau Semeniuta, Aliaksei Severyn and Sylvain Gelly, On Accurate Evaluation of GANs for Language Generation
  26. Tatjana Chavdarova, Sebastian Stich, Martin Jaggi and Francois Fleuret, Stochastic Variance Reduced Gradient Optimization of Generative Adversarial Networks
  27. Philip Botros and Jakub Tomczak, Hierarchical VampPrior Variational Fair Auto-Encoder
  28. Adji Bousso Dieng, Yoon Kim, Alexander Rush and David Blei, Avoiding Latent Variable Collapse with Generative Skip Models
  29. Dhruv Madeka, Lucas Swiniarski, Dean Foster, Leonid Razoumov, Ruofeng Wen and Kari Torkkola, Sample Path Generation for Probabilistic Demand Forecasting
  30. Kimin Lee, Kibok Lee, Honglak Lee and Jinwoo Shin, A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
  31. Alejandro Mottini, Alix Lhéritier and Rodrigo Acuna-Agost, Airline Passenger Name Record Generation using Generative Adversarial Networks
  32. Hao He, Hao Wang, Guang-He Lee and Yonglong Tian, Bayesian Modelling and Monte Carlo Inference for GAN
  33. Yu Bai, Tengyu Ma and Andrej Risteski, Approximability of Discriminators Implies Diversity in GANs
  34. Robin Tibor Schirrmeister, Patryk Chrabaszcz, Frank Hutter and Tonio Ball, Generative Reversible Networks
  35. Thanh-Tung Hoang, Truyen Tran and Svetha Venkatesh, On catastrophic forgetting and mode collapse in Generative Adversarial Networks
  36. Louis Tiao, Edwin Bonilla and Fabio Ramos, Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
  37. Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang, Semi-crowdsourced Clustering with Deep Generative Models
  38. Michel Besserve, Remy Sun and Bernhard Schoelkopf, Intrinsic disentanglement: an invariance view for deep generative models
  39. Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang and Christopher Pal, The Generative Fashion Dataset and Challenge
  40. Mahdi Azarafrooz, Xuan Zhao and Sepehr Masouleh, On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines
  41. Chao Du, Kun Xu, Chongxuan Li, Jun Zhu and Bo Zhang, Learning Implicit Generative Models by Teaching Explicit Ones
  42. Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet and Sylvain Gelly, Assessing Generative Models via Precision and Recall
  43. Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami and Yee Whye Teh, Neural Processes
  44. Laëtitia Shao, Jiaming Song, Aditya Grover and Stefano Ermon, Markov Chain Monte Carlo for Learning Belief Networks
  45. Priyank Jaini, Pascal Poupart and Yaoliang Yu, Deep Homogeneous Mixture Models : Representation, Separation, and Approximation
  46. Andrew Jesson, Cecile Low-Kam, Florian Soudan and Nicolas Chapados, Adversarially Learned Mixture Model
  47. Yingzhen Li, John Bradshaw and Yash Sharma, Are Generative Classifiers More Robust to Adversarial Attacks?
  48. Sachin Kumar and Yulia Tsvetkov, Machine Translation with Continuous Outputs
  49. Mohamed Ishmael Diwan Belghazi, Sai Rajeswar Mudumba, Olivier Mastropietro, Jovana Mitrovic, Negar Rostamzadeh and Aaron Courville, Hierarchical Adversarially Learned Inference
  50. Hareesh Bahuleyan, Lili Mou, Olga Vechtomova and Pascal Poupart, Variational Attention for Sequence-to-Sequence Models
  51. Guoqing Zheng, Yiming Yang and Jamie Carbonell, Convolutional Normalizing Flows
  52. Guoqing Zheng, Yiming Yang and Jaime Carbonell, Asymmetric Variational Autoencoders
  53. Septimia Sarbu, Riccardo Volpi, Alexandra Peste and Luigi Malagò, Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
  54. Xianglei Xing, Ruiqi Gao, Tian Han, Songchun Zhu and Yingnian Wu, Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
  55. Rui Shu, Hung Bui, Shengjia Zhao, Mykel Kochenderfer and Stefano Ermon, Amortized Inference Regularization
  56. Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap and James Davidson, Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  57. Zichao Wang, Andrew Lan, Weili Nie, Andrew Waters, Phillip Grimaldi and Richard Baraniuk, QG-Net: A Data-Driven Question Generation Model for Educational Content
  58. Aditya Grover and Stefano Ermon, Variational Compressive Sensing using Uncertainty Autoencoders
  59. Zain Shah, Generative Buyer-Seller Adversarial Networks
  60. Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt and David Duvenaud, Black-box ODE Solvers as a Modeling Primitive
  61. Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann and Eric Xing, 2d-Geometric Generalization Based Zero-Shot Intelligence Metric for Internal Consistency Evaluation
  62. Chelsea Finn, Kelvin Xu and Sergey Levine, Probabilistic Model-Agnostic Meta-Learning
  63. Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré and Taco S. Cohen, Explorations in Homeomorphic Variational Auto-Encoding
  64. Ishaan Gulrajani, Colin Raffel and Luke Metz, Towards GAN Benchmarks Which Require Generalization
  65. Akshat Dave, Anil Kumar Vadathya, Ramana Subramanyam and Kaushik Mitra, Solving Inverse Problems in Compressive Imaging using Deep Autoregressive Model
  66. Thanh Nguyen, Raymond Wong and Chinmay Hegde, Autoencoders Learn Generative Linear Models