Abstract is missing.
- Revisiting Auxiliary Latent Variables in Generative ModelsDieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath. [doi]
- Deep Random Splines for Point Process Intensity EstimationGabriel Loaiza-Ganem, John P. Cunningham. [doi]
- Point Cloud GANChun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov. [doi]
- Discrete Flows: Invertible Generative Models of Discrete DataDustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole. [doi]
- Visualizing and Understanding GANsDavid Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba 0001. [doi]
- Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous EnvironmentsÐorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer. [doi]
- WiSE-ALE: Wide Sample Estimator for Aggregate Latent EmbeddingShuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen J. Roberts. [doi]
- A Learned Representation for Scalable Vector GraphicsRaphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens. [doi]
- Disentangling Content and Style via Unsupervised Geometry DistillationWayne Wu, Kaidi Cao, Cheng Li 0009, Chen Qian 0006, Chen Change Loy. [doi]
- Improved Adversarial Image CaptioningPierre L. Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Tom Sercu. [doi]
- Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy MinimizationSam Wiseman. [doi]
- Learning to Defense by Learning to AttackZhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao. [doi]
- Deep Generative Models for Generating Labeled GraphsShuangfei Fan, Bert Huang. [doi]
- AlignFlow: Learning from multiple domains via normalizing flowsAditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon. [doi]
- Understanding Posterior Collapse in Generative Latent Variable ModelsJames Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi 0002. [doi]
- Generating Molecules via Chemical ReactionsJohn Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato. [doi]
- Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selectionTom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cícero Nogueira dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan. [doi]
- Adversarial Mixup ResynthesizersChristopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal. [doi]
- Interactive Image Generation Using Scene GraphsGaurav Mittal, Shubham Agrawal, Anuva Agarwal, Sushant Mehta, Tanya Marwah. [doi]
- Correlated Variational Auto-EncodersDa Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi. [doi]
- HYPE: Human-eYe Perceptual Evaluation of Generative ModelsSharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein. [doi]
- Structured Prediction using cGANs with Fusion DiscriminatorFaisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan L. Yuille. [doi]
- FVD: A new Metric for Video GenerationThomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly. [doi]
- Context Mover's Distance & Barycenters: Optimal transport of contexts for building representationsSidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi. [doi]
- Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour CloningSeyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel. [doi]
- Variational autoencoders trained with q-deformed lower boundsSeptimia Sârbu, Luigi Malagò. [doi]
- Generating Diverse High-Resolution Images with VQ-VAEAli Razavi, Aäron Van Den Oord, Oriol Vinyals. [doi]
- A RAD approach to deep mixture modelsLaurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle. [doi]
- Compositional GAN (Extended Abstract): Learning Image-Conditional Binary CompositionSamaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell. [doi]
- Unsupervised Demixing of Structured Signals from Their Superposition Using GANsMohammadreza Soltani, Swayambhoo Jain, Abhinav V. Sambasivan. [doi]
- Fully differentiable full-atom protein backbone generationNamrata Anand, Raphael Eguchi, Po-Ssu Huang. [doi]
- On Scalable and Efficient Computation of Large Scale Optimal TransportYujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha. [doi]
- DIVA: Domain Invariant Variational AutoencoderMaximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling. [doi]
- A Seed-Augment-Train Framework for Universal Digit ClassificationVinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri. [doi]
- Smoothing Nonlinear Variational Objectives with Sequential Monte CarloAntonio Moretti, Zizhao Wang, Luhuan Wu, Itsik Pe'er. [doi]
- Generative Models for Graph-Based Protein DesignJohn Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola. [doi]
- Dual Space Learning with variational AutoencodersHirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo. [doi]
- Perceptual Generative AutoencodersZijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull. [doi]
- Adjustable Real-time Style TransferMohammad Babaeizadeh, Golnaz Ghiasi. [doi]
- Bias Correction of Learned Generative Models via Likelihood-free Importance WeightingAditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon. [doi]
- Storyboarding of Recipes: Grounded Contextual GenerationKhyathi Raghavi Chandu, Eric Nyberg, Alan W. Black. [doi]
- On the relationship between Normalising Flows and Variational- and Denoising AutoencodersAlexey A. Gritsenko, Jasper Snoek, Tim Salimans. [doi]