| Iterative amortized inference J Marino, Y Yue, S Mandt International Conference on Machine Learning, 3403-3412, 2018 | 135 | 2018 |
| Probabilistic video generation using holistic attribute control J He, A Lehrmann, J Marino, G Mori, L Sigal Proceedings of the European conference on computer vision (ECCV), 452-467, 2018 | 72 | 2018 |
| Hierarchical autoregressive modeling for neural video compression R Yang, Y Yang, J Marino, S Mandt arXiv preprint arXiv:2010.10258, 2020 | 29 | 2020 |
| A general method for amortizing variational filtering J Marino, M Cvitkovic, Y Yue Advances in neural information processing systems 31, 2018 | 28 | 2018 |
| Predictive coding, variational autoencoders, and biological connections J Marino Neural Computation 34 (1), 1-44, 2022 | 22 | 2022 |
| Iterative amortized policy optimization J Marino, A Piché, AD Ialongo, Y Yue Advances in Neural Information Processing Systems 34, 15667-15681, 2021 | 16 | 2021 |
| Variational autoencoders with jointly optimized latent dependency structure J He, Y Gong, J Marino, G Mori, A Lehrmann International Conference on Learning Representations, 2019 | 13 | 2019 |
| Improving sequential latent variable models with autoregressive flows J Marino, L Chen, J He, S Mandt Symposium on advances in approximate bayesian inference, 1-16, 2020 | 9 | 2020 |
| Learning to infer J Marino, Y Yue, S Mandt | 7 | 2018 |
| Beyond Target Networks: Improving Deep -learning with Functional Regularization A Piché, V Thomas, J Marino, GM Marconi, C Pal, ME Khan arXiv preprint arXiv:2106.02613, 2021 | 5 | 2021 |
| Insights from generative modeling for neural video compression R Yang, Y Yang, J Marino, S Mandt arXiv preprint arXiv:2107.13136, 2021 | 4 | 2021 |
| An inference perspective on model-based reinforcement learning J Marino, Y Yue ICML Workshop on Generative Modeling and Model-Based Reasoning for Robotics …, 2019 | 4 | 2019 |
| Deep generative video compression with temporal autoregressive transforms R Yang, Y Yang, J Marino, Y Yang, S Mandt ICML 2020 Workshop on Invertible Neural Networks, Normalizing Flows, and …, 2020 | 2 | 2020 |
| On the design of variational rl algorithms J Marino, A Piché, Y Yue NeurIPS Workshop on Deep Reinforcement Learning, 2019 | 2 | 2019 |
| Taxonomic Curriculum Learning for Fine-Grained Object Recognition J Marino Semantic Scholar, 2016 | 2 | 2016 |
| Sequential Autoregressive Flow-Based Policies A Guerra, J Marino ICML workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2020 | 1 | 2020 |
| A General Framework for Amortizing Variational Filtering J Marino, M Cvitkovic, Y Yue ICML 2018 Workshop Theor. Found. Appl. Deep Gener. Model, 2018 | 1 | 2018 |
| Learned Feedback & Feedforward Perception & Control JL Marino California Institute of Technology, 2021 | | 2021 |
| SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS R Yang, Y Yang, J Marino, S Mandt Neural Compression: From Information Theory to Applications--Workshop@ ICLR 2021, 0 | | |
| Generative Video Compression as Hierarchical Variational Inference R Yang, Y Yang, J Marino, S Mandt Third Symposium on Advances in Approximate Bayesian Inference, 0 | | |
Yisong YueCalifornia Institute of Technology; Latitude AI在 caltech.edu 的电子邮件经过验证
