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pro vyhledávání: '"Markowitz, Jared"'
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac3a07010bb53d9e1f8c632379468969
http://arxiv.org/abs/2208.09106
http://arxiv.org/abs/2208.09106
Autor:
Staley, Edward W., Markowitz, Jared
One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution. This latter property is often exploited when introducing information bott
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f1732db775cc8c28a31d5cb7fb67466
Autor:
Staley, Edward W., Ashcraft, Chace, Stoler, Benjamin, Markowitz, Jared, Gautam Vallabha, Ratto, Christopher, Katyal, Kapil D.
Publikováno v:
Gautam Vallabha
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites of games that have common interfaces but little overlap in their perce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2aca1a08368ef5c6d8a6610282f827a
http://arxiv.org/abs/2112.00583
http://arxiv.org/abs/2112.00583
Autor:
Katyal, Kapil, Gao, Yuxiang, Markowitz, Jared, Pohland, Sara, Rivera, Corban, Wang, I-Jeng, Huang, Chien-Ming
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional indivi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::551048303a930c3f11400f55e3753b4a
http://arxiv.org/abs/2012.12291
http://arxiv.org/abs/2012.12291
This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent's pieces a priori but may reveal a subset of them through
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::527619f3ff53b613f33c576d33ec1bc9
http://arxiv.org/abs/1811.03119
http://arxiv.org/abs/1811.03119
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation. For both non-novel and novel image classes we compare multiple formulations of the pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7871dd20949b3f5a93fb3ced20ed3407