Zobrazeno 1 - 10
of 5 287
pro vyhledávání: '"Zisman, A."'
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (
Externí odkaz:
http://arxiv.org/abs/2411.01958
Autor:
Nikulin, Alexander, Zisman, Ilya, Zemtsov, Alexey, Sinii, Viacheslav, Kurenkov, Vladislav, Kolesnikov, Sergey
Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back
Externí odkaz:
http://arxiv.org/abs/2406.08973
Autor:
Tripp, Charles Edison, Perr-Sauer, Jordan, Gafur, Jamil, Nag, Amabarish, Purkayastha, Avi, Zisman, Sagi, Bensen, Erik A.
Addressing the so-called ``Red-AI'' trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by node-level watt-meters, of training various fully connected neural network ar
Externí odkaz:
http://arxiv.org/abs/2403.08151
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined
Externí odkaz:
http://arxiv.org/abs/2312.13327
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context RL methods are limited by their strict requirements for data, which n
Externí odkaz:
http://arxiv.org/abs/2312.12275
Autor:
Nikulin, Alexander, Kurenkov, Vladislav, Zisman, Ilya, Agarkov, Artem, Sinii, Viacheslav, Kolesnikov, Sergey
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed t
Externí odkaz:
http://arxiv.org/abs/2312.12044
Publikováno v:
AAMAS '23, Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (May 2023) Pages 49-57
The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private infor
Externí odkaz:
http://arxiv.org/abs/2306.08419
Autor:
Lior Zisman Zalis, Diego Posada
Publikováno v:
Re-visiones, Iss 9 (2024)
Externí odkaz:
https://doaj.org/article/ced1624e925240abbe6f7bb58140e168
Autor:
Laine Halpern Zisman
Publikováno v:
New Area Studies, Vol 4, Iss 2 (2024)
Queer and transgender individuals often encounter barriers when attempting to build their families, Beginning by identifying barriers that 2SLGBTQ+ people face in their fertility and reproductive journeys -- including limited access to information, i
Externí odkaz:
https://doaj.org/article/57cdb7760e2446c5a8437ff2ae1d1af7
Autor:
Amir Haddad, Perach Chen Elkayam, Nili Stein, Ilan Feldhamer, Arnon Dov Cohen, Walid Saliba, Devy Zisman
Publikováno v:
Arthritis Research & Therapy, Vol 26, Iss 1, Pp 1-8 (2024)
Abstract Background Psoriatic arthritis (PsA) is a chronic, potentially debilitating inflammatory arthritis often associated with psoriasis. Understanding the epidemiology of PsA across diverse populations can provide valuable insights into its globa
Externí odkaz:
https://doaj.org/article/136039143e314672a35d8d5b925e7f52