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DGS@ICLR 2019: New Orleans, Louisiana, United States
- Deep Generative Models for Highly Structured Data, ICLR 2019 Workshop, New Orleans, Louisiana, United States, May 6, 2019. OpenReview.net 2019
Accepted Papers
- John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Generating Molecules via Chemical Reactions. - Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri:
A Seed-Augment-Train Framework for Universal Digit Classification. - Sam Wiseman:
Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization. - Mohammadreza Soltani, Swayambhoo Jain, Abhinav V. Sambasivan:
Unsupervised Demixing of Structured Signals from Their Superposition Using GANs. - Zhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defense by Learning to Attack. - Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. - Antonio Khalil Moretti, Zizhao Wang, Luhuan Wu, Itsik Pe'er:
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo. - Mohammad Babaeizadeh, Golnaz Ghiasi:
Adjustable Real-time Style Transfer. - Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cícero Nogueira dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan:
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection. - Alexey A. Gritsenko, Jasper Snoek, Tim Salimans:
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders. - Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments. - Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell:
Compositional GAN (Extended Abstract): Learning Image-Conditional Binary Composition. - Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen J. Roberts:
WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding. - Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Learning from multiple domains via normalizing flows. - Hirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo:
Dual Space Learning with variational Autoencoders. - Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. - Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov:
Point Cloud GAN. - Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. - Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein:
HYPE: Human-eYe Perceptual Evaluation of Generative Models. - John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. - Faisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan L. Yuille:
Structured Prediction using cGANs with Fusion Discriminator. - Gaurav Mittal, Shubham Agrawal, Anuva Agarwal, Sushant Mehta, Tanya Marwah:
Interactive Image Generation Using Scene Graphs. - Namrata Anand, Raphael Eguchi, Po-Ssu Huang:
Fully differentiable full-atom protein backbone generation. - Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoder. - Wayne Wu, Kaidi Cao, Cheng Li, Chen Qian, Chen Change Loy:
Disentangling Content and Style via Unsupervised Geometry Distillation. - Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal:
Adversarial Mixup Resynthesizers. - Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Revisiting Auxiliary Latent Variables in Generative Models. - Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. - James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi:
Understanding Posterior Collapse in Generative Latent Variable Models. - Khyathi Raghavi Chandu, Eric Nyberg, Alan W. Black:
Storyboarding of Recipes: Grounded Contextual Generation. - David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba:
Visualizing and Understanding GANs. - Gabriel Loaiza-Ganem, John P. Cunningham:
Deep Random Splines for Point Process Intensity Estimation. - Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. - Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel:
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. - Pierre L. Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Tom Sercu:
Improved Adversarial Image Captioning. - Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. - Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull:
Perceptual Generative Autoencoders. - Ali Razavi, Aäron van den Oord, Oriol Vinyals:
Generating Diverse High-Resolution Images with VQ-VAE. - Shuangfei Fan, Bert Huang:
Deep Generative Models for Generating Labeled Graphs. - Septimia Sârbu, Luigi Malagò:
Variational autoencoders trained with q-deformed lower bounds. - Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. - Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
FVD: A new Metric for Video Generation.
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