ICLRW 2019
107 papers
A Learned Representation for Scalable Vector Graphics
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens A RAD Approach to Deep Mixture Models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle A Seed-Augment-Train Framework for Universal Digit Classification
Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro Adjustable Real-Time Style Transfer
Mohammad Babaeizadeh, Golnaz Ghiasi Adversarial Learning of General Transformations for Data Augmentation
Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed, Marco Pedersoli Adversarial Mixup Resynthesizers
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal Bias Correction of Learned Generative Models via Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon Connecting the Dots Between MLE and RL for Sequence Generation
Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning
Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, Sosuke Kobayashi Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole Disentangling Factors of Variations Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar R¨¨ätsch, Bernhard Schölkopf, Olivier Bachem DIVA: Domain Invariant Variational Autoencoder
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling Dual Space Learning with Variational Autoencoders
Hirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo Enhancing Experimental Signals in Single-Cell RNA-Sequencing Data Using Graph Signal Processing
Daniel B. Burkhardt, Jay S. Stanley Iii, Ana Luisa Perdigoto, Scott A. Gigante, Kevan C. Herold, Guy Wolf, Antonio J. Giraldez, David van Dijk, Smita Krishnaswamy Explanation-Based Attention for Semi-Supervised Deep Active Learning
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa Few-Shot Regression via Learned Basis Functions
Yi Loo, Swee Kiat Lim, Gemma Roig, Ngai-Man Cheung FVD: A New Metric for Video Generation
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly Generating Molecules via Chemical Reactions
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato Generative Models for Graph-Based Protein Design
John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola Heuristics for Image Generation from Scene Graphs
Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang HYPE: Human-eYe Perceptual Evaluation of Generative Models
Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein Improved Adversarial Image Captioning
Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Tom Sercu Improving Sample Complexity with Observational Supervision
Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Re Interactive Image Generation Using Scene Graphs
Gaurav Mittal, Shubham Agrawal, Anuva Agarwal, Sushant Mehta, Tanya Marwah 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 Learnability for the Information Bottleneck
Tailin Wu, Ian Fischer, Isaac Chuang, Max Tegmark Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories
Jeffrey Ling, Nicholas FitzGerald, Livio Baldini Soares, David Weiss, Tom Kwiatkowski Learning from Samples of Variable Quality
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf Learning to Defense by Learning to Attack
Zhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine Online Semi-Supervised Learning with Bandit Feedback
Mikhail Yurochkin, Sohini Upadhyay, Djallel Bouneffouf, Mayank Agarwal, Yasaman Khazaeni Passage Ranking with Weak Supervision
Peng Xu, Xiaofei Ma, Ramesh Nallapati, Bing Xiang Perceptual Generative Autoencoders
Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull Point Cloud GAN
Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov Reference-Based Variational Autoencoders
Adrià Ruiz, Oriol Martinez, Xavier Binefa, Jakob Verbeek Revisiting Auxiliary Latent Variables in Generative Models
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath Robust Reinforcement Learning for Autonomous Driving
Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur Structured Prediction Using cGANs with Fusion Discriminator
Faisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan Yuille Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
Mihir Kale, Aditya Siddhant, Sreyashi Nag, Radhika Parik, Anthony Tomasic, Matthias Grabmair Visualizing and Understanding GANs
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba Weakly Semi-Supervised Neural Topic Models
Ian Gemp, Ramesh Nallapati, Ran Ding, Feng Nan, Bing Xiang WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding
Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts