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Adam Stooke
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Conference and Workshop Papers
- 2024
- [c7]Khe Chai Sim, Zhouyuan Huo, Tsendsuren Munkhdalai, Nikhil Siddhartha, Adam Stooke, Zhong Meng, Bo Li, Tara N. Sainath:
A Comparison of Parameter-Efficient ASR Domain Adaptation Methods for Universal Speech and Language Models. ICASSP 2024: 6900-6904 - [c6]Rohit Prabhavalkar, Zhong Meng, Weiran Wang, Adam Stooke, Xingyu Cai, Yanzhang He, Arun Narayanan, Dongseong Hwang, Tara N. Sainath, Pedro J. Moreno:
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models. ICASSP 2024: 11816-11820 - 2022
- [c5]Adam Stooke, Khe Chai Sim, Mason Chua, Tsendsuren Munkhdalai, Trevor Strohman:
Internal Language Model Personalization of E2E Automatic Speech Recognition Using Random Encoder Features. SLT 2022: 213-220 - 2021
- [c4]Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin:
Decoupling Representation Learning from Reinforcement Learning. ICML 2021: 9870-9879 - 2020
- [c3]Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. ICML 2020: 9133-9143 - [c2]Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. NeurIPS 2020 - 2017
- [c1]Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. NIPS 2017: 2753-2762
Informal and Other Publications
- 2024
- [i11]Rohit Prabhavalkar, Zhong Meng, Weiran Wang, Adam Stooke, Xingyu Cai, Yanzhang He, Arun Narayanan, Dongseong Hwang, Tara N. Sainath, Pedro J. Moreno:
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models. CoRR abs/2402.17184 (2024) - 2023
- [i10]Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Zhong Meng, CJ Zheng, Yanzhang He, Tara N. Sainath, Pedro Moreno Mengibar:
Massive End-to-end Models for Short Search Queries. CoRR abs/2309.12963 (2023) - 2021
- [i9]Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michaël Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, Wojciech Marian Czarnecki:
Open-Ended Learning Leads to Generally Capable Agents. CoRR abs/2107.12808 (2021) - 2020
- [i8]Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. CoRR abs/2004.14990 (2020) - [i7]Adam Stooke, Valentin Dalibard, Siddhant M. Jayakumar, Wojciech M. Czarnecki, Max Jaderberg:
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning. CoRR abs/2006.15223 (2020) - [i6]Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. CoRR abs/2007.03964 (2020) - [i5]Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin:
Decoupling Representation Learning from Reinforcement Learning. CoRR abs/2009.08319 (2020) - 2019
- [i4]Adam Stooke, Pieter Abbeel:
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch. CoRR abs/1909.01500 (2019) - 2018
- [i3]Adam Stooke, Pieter Abbeel:
Accelerated Methods for Deep Reinforcement Learning. CoRR abs/1803.02811 (2018) - 2017
- [i2]Adam Stooke, Pieter Abbeel:
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism. CoRR abs/1710.04162 (2017) - 2016
- [i1]Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. CoRR abs/1611.04717 (2016)
Coauthor Index
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