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pro vyhledávání: '"Markowitz, Jared"'
Autor:
Markowitz, Jared, Staley, Edward W.
To facilitate efficient learning, policy gradient approaches to deep reinforcement learning (RL) are typically paired with variance reduction measures and strategies for making large but safe policy changes based on a batch of experiences. Natural po
Externí odkaz:
http://arxiv.org/abs/2311.05846
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:
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:
http://arxiv.org/abs/2205.01235
Autor:
Staley, Edward W., Ashcraft, Chace, Stoler, Benjamin, Markowitz, Jared, Vallabha, Gautam, Ratto, Christopher, Katyal, Kapil D.
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:
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:
http://arxiv.org/abs/2012.12291
Searching for small objects in large images is a task that is both challenging for current deep learning systems and important in numerous real-world applications, such as remote sensing and medical imaging. Thorough scanning of very large images is
Externí odkaz:
http://arxiv.org/abs/2012.06509
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:
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:
http://arxiv.org/abs/1712.03151
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 119-123).
In this thesis we present a data-driven neuromuscular model of h
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 119-123).
In this thesis we present a data-driven neuromuscular model of h
Externí odkaz:
http://hdl.handle.net/1721.1/83822
Publikováno v:
Proceedings of SPIE; June 2023, Vol. 12538 Issue: 1 p125380O-125380O-8, 1128429p