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pro vyhledávání: '"Venuto, David"'
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In this paper, we aim to leverage these capabilities to support the traini
Externí odkaz:
http://arxiv.org/abs/2402.04764
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learning, however, a key challenge is that available data of sequential decis
Externí odkaz:
http://arxiv.org/abs/2211.13337
Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments. Whi
Externí odkaz:
http://arxiv.org/abs/2108.02096
Autor:
Venuto, David, Chakravorty, Jhelum, Boussioux, Leonard, Wang, Junhao, McCracken, Gavin, Precup, Doina
Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned
Externí odkaz:
http://arxiv.org/abs/2002.09043
Autor:
Venuto, David, Boussioux, Leonard, Wang, Junhao, Dali, Rola, Chakravorty, Jhelum, Bengio, Yoshua, Precup, Doina
Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \t
Externí odkaz:
http://arxiv.org/abs/1909.11228
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicates which element of the pair is better, or if there is no significant d
Externí odkaz:
http://arxiv.org/abs/1401.8008
Autor:
Venuto, David1, Bourque, Guillaume1,2,3 guil.bourque@mcgill.ca
Publikováno v:
Development, Growth & Differentiation. Jan2018, Vol. 60 Issue 1, p53-62. 10p.
Akademický článek
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Autor:
Kanagaratham, Cynthia, Chiwara, Victoria, Ho, Bianca, Moussette, Sanny, Youssef, Mina, Venuto, David, Jeannotte, Lucie, Bourque, Guillaume, de Sanctis, Juan Bautista, Radzioch, Danuta, Naumova, Anna K.
Publikováno v:
Mammalian Genome; Apr2018, Vol. 29 Issue 3/4, p281-298, 18p