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David Szepesvari
David Szepesvari
Research Engineer, DeepMind
Подтвержден адрес электронной почты в домене uwaterloo.ca
Название
Процитировано
Процитировано
Год
Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton
Advances in neural information processing systems 29, 2016
6212016
Grounded language learning in a simulated 3d world
KM Hermann, F Hill, S Green, F Wang, R Faulkner, H Soyer, D Szepesvari, ...
arXiv preprint arXiv:1706.06551, 2017
351*2017
When should agents explore?
M Pislar, D Szepesvari, G Ostrovski, D Borsa, T Schaul
arXiv preprint arXiv:2108.11811, 2021
362021
Reward-Respecting Subtasks for Model-Based Reinforcement Learning
RS Sutton, MC Machado, GZ Holland, F Timbers, D Szepesvari, B Tanner, ...
arXiv preprint arXiv:2202.03466, 2022
252022
Adapting behaviour for learning progress
T Schaul, D Borsa, D Ding, D Szepesvari, G Ostrovski, W Dabney, ...
arXiv preprint arXiv:1912.06910, 2019
182019
k. kavukcuoglu, and GE Hinton,“
SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari
Attend, infer, repeat: Fast scene understanding with generative models,” in …, 2016
82016
Modulating agent behavior to optimize learning progress
T Schaul, DL Borsa, F Ding, D Szepesvari, G Ostrovski, S Osindero, ...
US Patent 12,061,964, 2024
22024
Attend, infer, repeat: Fast scene understanding with generative models
N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton
Advances in, 2016
22016
A statistical analysis of the aggregation of crowdsourced labels
D Szepesvari
University of Waterloo, 2015
22015
Mitigating the curse of horizon in Monte-Carlo returns
A Ayoub, D Szepesvari, F Zanini, B Chan, D Gupta, BC da Silva, ...
Reinforcement Learning Conference, 2024
12024
Reward-respecting subtasks for model-based reinforcement learning (abstract reprint)
RS Sutton, MC Machado, GZ Holland, D Szepesvari, F Timbers, B Tanner, ...
Proceedings of the AAAI Conference on Artificial Intelligence 38 (20), 22713 …, 2024
2024
Attend, Infer, Repeat: Fast Scene Understanding with Deep Generative Models
SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, K Kavukcuoglu, ...
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