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Publication search results
found 21 matches
- 2024
- Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepert, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Youngwoon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pannag R. Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang, Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Guaman Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, et al.:
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. CoRR abs/2403.12945 (2024) - Cody Wild, Jesper Anderson:
Uncovering Layer-Dependent Activation Sparsity Patterns in ReLU Transformers. CoRR abs/2407.07848 (2024) - 2023
- Jeremy A. Collins, Cody Houff, Patrick Grady, Charles C. Kemp:
Visual Contact Pressure Estimation for Grippers in the Wild. IROS 2023: 10947-10954 - Jeremy A. Collins, Cody Houff, Patrick Grady, Charles C. Kemp:
Visual Contact Pressure Estimation for Grippers in the Wild. CoRR abs/2303.07344 (2023) - 2022
- Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Jun Shern Chan, Daniel del Castillo, Tom Lieberum:
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback. CoRR abs/2204.07123 (2022) - Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. CoRR abs/2205.07886 (2022) - 2021
- Cynthia Chen, Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. NeurIPS Datasets and Benchmarks 2021 - Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Jun Shern Chan, Daniel del Castillo, Tom Lieberum:
Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback. NeurIPS (Competition and Demos) 2021: 259-272 - Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell:
Clusterability in Neural Networks. CoRR abs/2103.03386 (2021) - Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca D. Dragan:
The MineRL BASALT Competition on Learning from Human Feedback. CoRR abs/2107.01969 (2021) - Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell:
Detecting Modularity in Deep Neural Networks. CoRR abs/2110.08058 (2021) - 2020
- Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. ICLR 2020 - Daniel Filan, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell:
Neural Networks are Surprisingly Modular. CoRR abs/2003.04881 (2020) - 2019
- Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk:
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation. ACM Trans. Softw. Eng. Methodol. 28(4): 19:1-19:29 (2019) - Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard E. Harang:
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. USENIX Security Symposium 2019: 303-320 - Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard E. Harang:
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. CoRR abs/1903.05700 (2019) - Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. CoRR abs/1905.10615 (2019) - 2018
- Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk:
An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. ASE 2018: 832-837 - Joshua Saxe, Richard E. Harang, Cody Wild, Hillary Sanders:
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. IEEE Symposium on Security and Privacy Workshops 2018: 8-14 - Joshua Saxe, Richard E. Harang, Cody Wild, Hillary Sanders:
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. CoRR abs/1804.05020 (2018) - Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk:
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation. CoRR abs/1812.08693 (2018)
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