Papers

• Imagen Video: High Definition Video Generation with Diffusion Models    
  Jonathan Ho*, William Chan*, Chitwan Saharia*, Jay Whang*, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, Tim Salimans*
  Preprint.

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding    
  Chitwan Saharia*, William Chan*, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David J Fleet†, Mohammad Norouzi*.
  Conference on Neural Information Processing Systems (NeurIPS), 2022.
  Chosen as one of 13 Outstanding Papers.

• Deblurring via Stochastic Refinement    
  Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis,
  Peyman Milanfar.
  Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  Chosen for Oral Presentation.

• Neural Distributed Source Coding     [code]
  Jay Whang, Anish Acharya, Hyeji Kim, Alexandros G. Dimakis.
  Preprint.

• Composing Normalizing Flows for Inverse Problems
  Jay Whang, Erik M. Lindgren, Alexandros G. Dimakis.
  The 38th International Conference on Machine Learning (ICML), 2021.
  Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling
  (Short version accepted to NeurIPS 2020 Workshop on Deep Learning and Inverse Problems)

• Model-Based Deep Learning
  Nir Shlezinger, Jay Whang, Yonina Eldar, Alex Dimakis.
  Preprint.

• Solving Inverse Problems with a Flow-based Noise Model
  Jay Whang, Qi Lei, Alexandros G. Dimakis.
  The 38th International Conference on Machine Learning (ICML), 2021.
  (Short version accepted to NeurIPS 2020 Workshop on Deep Learning and Inverse Problems)

• Model-based Deep Learning: Key Approaches And Design Guidelines
  Nir Shlezinger, Jay Whang, Yonina Eldar, Alex Dimakis.
  Audience Choice Award at IEEE Data Science & Learning Workshop (DSLW 2021).

• Training Variational Autoencoders with Buffered Stochastic Variational Inference
  Rui Shu, Hung Bui, Jay Whang, Stefano Ermon.
  The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

• Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
  Ramtin Keramati*, Jay Whang*, Patrick Cho* and Emma Brunskill.
  Preprint.

• Strategic Exploration in Object-Oriented Reinforcement Learning
  Ramtin Keramati*, Jay Whang*, Patrick Cho* and Emma Brunskill.
  The 35th International Conference on Machine Learning (ICML) Workshop on Exploration in RL, 2018.

(* equal contribution) († core contribution)