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Bottleneck Transformers for Visual Recognition.

, , , , , and . CVPR, page 16519-16529. Computer Vision Foundation / IEEE, (2021)

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Constrained Policy Optimization., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 22-31. PMLR, (2017)Alpha-Beta Divergences Discover Micro and Macro Structures in Data., , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 796-804. JMLR.org, (2015)rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch., and . CoRR, (2019)Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 2722-2730. PMLR, (2019)Preferences Implicit in the State of the World., , , , and . ICLR (Poster), OpenReview.net, (2019)Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3408-3417. PMLR, (2019)Learning Factor Graphs in Polynomial Time and Sample Complexity., , and . J. Mach. Learn. Res., (2006)End-to-End Training of Deep Visuomotor Policies., , , and . J. Mach. Learn. Res., (2016)Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model., , , and . CoRR, (2019)Learning contact-rich manipulation skills with guided policy search., , and . ICRA, page 156-163. IEEE, (2015)