DGS@ICLR

Publications

2019

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