| Photorealistic text-to-image diffusion models with deep language understanding C Saharia, W Chan, S Saxena, L Li, J Whang, EL Denton, K Ghasemipour, ... Advances in Neural Information Processing Systems 35, 36479-36494, 2022 | 4489 | 2022 |
| Imagen Video: High Definition Video Generation with Diffusion Models J Ho, W Chan, C Saharia, J Whang, R Gao, A Gritsenko, DP Kingma, ... arXiv preprint arXiv:2210.02303, 2022 | 1071 | 2022 |
| Model-Based Deep Learning N Shlezinger, J Whang, YC Eldar, AG Dimakis Proceedings of the IEEE, 2023 | 309 | 2023 |
| Deblurring via stochastic refinement J Whang, M Delbracio, H Talebi, C Saharia, AG Dimakis, P Milanfar Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 238 | 2022 |
| Solving Inverse Problems with a Flow-based Noise Model J Whang, Q Lei, A Dimakis International Conference on Machine Learning (ICML), 2021 | 64* | 2021 |
| Composing Normalizing Flows for Inverse Problems J Whang, EM Lindgren, AG Dimakis International Conference on Machine Learning (ICML), 2021 | 63* | 2021 |
| Model-Based Deep Learning: Key Approaches and Design Guidelines N Shlezinger, J Whang, YC Eldar, AG Dimakis 2021 IEEE Data Science and Learning Workshop (DSLW), 1-6, 2021 | 39 | 2021 |
| Strategic Object Oriented Reinforcement Learning R Keramati, J Whang, P Cho, E Brunskill arXiv preprint arXiv:1806.00175, 2018 | 22 | 2018 |
| Neural distributed source coding J Whang, A Nagle, A Acharya, H Kim, AG Dimakis IEEE Journal on Selected Areas in Information Theory, 2024 | 21 | 2024 |
| Using generative models for semi-supervised learning DD Adiwardana, A Matsukawa, J Whang Stanford reports, 2017 | 10 | 2017 |
| Approximate Probabilistic Inference with Composed Flows J Whang, EM Lindgren, AG Dimakis NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, arXiv: 2002.11743, 2020 | 6 | 2020 |
| Training Variational Autoencoders with Buffered Stochastic Variational Inference R Shu, HH Bui, J Whang, S Ermon International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 5 | 2019 |
| Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning R Keramati, J Whang, P Cho, E Brunskill arXiv preprint arXiv:1806.00175, 2018 | 5 | 2018 |
| Exploring Batch Normalization in Recurrent Neural Networks J Whang, A Matsukawa | 2 | |
| GENERATING VIDEOS USING SEQUENCES OF GENERATIVE NEURAL NETWORKS J Ho, W Chan, C Saharia, JH Whang, T Salimans US Patent App. 18/400,856, 2024 | | 2024 |
| GENERATING IMAGES USING SEQUENCES OF GENERATIVE NEURAL NETWORKS C Saharia, W Chan, M Norouzi, S Saxena, Y Li, JH Whang, DJ Fleet, J Ho US Patent App. 18/624,960, 2024 | | 2024 |
| Generating images using sequences of generative neural networks C Saharia, W Chan, M Norouzi, S Saxena, Y Li, JH Whang, DJ Fleet, J Ho US Patent 11,978,141, 2024 | | 2024 |
| Generating videos using sequences of generative neural networks J Ho, W Chan, C Saharia, JH Whang, T Salimans US Patent 11,908,180, 2024 | | 2024 |
| Strategic Exploration in Object-Oriented Reinforcement Learning R Keramati*, J Whang*, P Cho*, E Brunskil ICML 2018 workshop on exploration in reinforcement learning, 2018 | | 2018 |
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityПотвърден имейл адрес: cs.stanford.edu
