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Accepted Papers

We are very excited to accept 42 fantastic papers for the first workshop on Deep Generative Models for Highly Structured Data. Special thanks to our wonderful program committee for their hard work in reviewing the submissions. The full proceedings will be available on OpenReview, and the papers will be presented as posters during the workshop.

See you all in New Orleans!

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