Accepted Papers

Note: Papers listed here are for archival purposes only and do not constitute as proceedings for this workshop.

  1. A-NICE-MC: Adversarial Training for MCMC Jiaming Song, Shengjia Zhao, Stefano Ermon
  2. ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks Igor Susmelj, Eirikur Agustsson, Radu Timofte
  3. Adversarial Inversion for Amortized Inference Zenna Tavares, Edgar Minasyan, Armando Solar Lezama
  4. Adversarial Variational Inference for Tweedie Compound Poisson Models Yaodong Yang, Sergey Demyanov, Yuanyuan Liu, Jun Wang
  5. Adversarially Learned Boundaries in Instance Segmentation Amy Zhang
  6. Approximate Inference with Amortised MCMC Yingzhen Li, Richard E. Turner, Qiang Liu
  7. Can GAN Learn Topological Features of a Graph? Weiyi Liu, Pin-Yu Chen, Hal Cooper, Min Hwan Oh, Sailung Yeung, Toyotaro Suzumura
  8. Conditional generation of multi-modal data using constrained embedding space mapping Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Md. A. Salam Khan and Ryuki Tachibana
  9. Deep Hybrid Discriminative-Generative Models for Semi-Supervised Learning Volodymyr Kuleshov, Stefano Ermon
  10. ELFI, a software package for likelihood-free inference Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skyten, Marko Järvenpää, Michael Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
  11. Flow-GAN: Bridging implicit and prescribed learning in generative models Aditya Grover, Manik Dhar, Stefano Ermon
  12. GANs Powered by Autoencoding — A Theoretic Reasoning Zhifei Zhang, Yang Song, and Hairong Qi
  13. Geometric GAN Jae Hyun Lim and Jong Chul Ye
  14. Gradient Estimators for Implicit Models Yingzhen Li, Richard E. Turner
  15. Implicit Manifold Learning on Generative Adversarial Networks Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau, Kelvin Shuangjian Zhang
  16. Implicit Variational Inference with Kernel Density Ratio Fitting Jiaxin Shi, Shengyang Sun, Jun Zhu
  17. Improved Network Robustness with Adversarial Critic Alexander Matyasko, Lap-Pui Chau
  18. Improved Training of Wasserstein GANs Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville
  19. Inference in differentiable generative models Matthew M. Graham and Amos J. Storkey
  20. Joint Training in Generative Adversarial Networks R Devon Hjelm, Athul Paul Jacob, Yoshua Bengio
  21. Latent Space GANs for 3D Point Clouds Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
  22. Likelihood Estimation for Generative Adversarial Networks Hamid Eghbal-zadeh, Gerhard Widmer
  23. Maximizing Independence with GANs for Non-linear ICA Philemon Brakel, Yoshua Bengio
  24. Non linear Mixed Effects Models: Bridging the gap between Independent Metropolis Hastings and Variational Inference Belhal Karimi
  25. Practical Adversarial Training with Empirical Distribution Ambrish Rawat, Mathieu Sinn, Maria-Irina Nicolae
  26. Recursive Cross-Domain Facial Composite and Generation from Limited Facial Parts Yang Song, Zhifei Zhang, Hairong Qi
  27. Resampled Proposal Distributions for Variational Inference and Learning Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredil, Dale Schuurmans, Stefano Ermon
  28. Rigorous Analysis of Adversarial Training with Empirical Distributions Mathieu Sinn, Ambrish Rawat, Maria-Irina Nicolae
  29. Robust Controllable Embedding of High-Dimensional Observations of Markov Decision Processes Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui
  30. Spectral Normalization for Generative Adversarial Network Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
  31. Stabilizing the Conditional Adversarial Network by Decoupled Learning Zhifei Zhang, Yang Song, and Hairong Qi
  32. Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth, Aurelien Lucchi, Sebastian Nowozin & Thomas Hofmann
  33. Stochastic Reconstruction of Three-Dimensional Porous Media using Generative Adversarial Networks Lukas Mosser, Olivier Dubrule, Martin J. Blunt
  34. The Amortized Bootstrap Eric Nalisnick, Padhraic Smyth
  35. The Numerics of GANs Lars Mescheder, Sebastian Nowozin, Andreas Geiger
  36. Towards the Use of Gaussian Graphical Models in Variational Autoencoders Alexandra Pește, Luigi Malagò
  37. Training GANs with Variational Statistical Information Minimization Michael Ghaben
  38. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros
  39. Unsupervised Domain Adaptation Using Approximate Label Matching Jordan T. Ash, Robert E. Schapire, Barbara E. Englhardt
  40. Variance Regularizing Adversarial Learning Karan Grewal, R Devon Hjelm, Yoshua Bengio
  41. Variational Representation Autoencoders to Reduce Mode Collapse in GANs Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton