Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Kang, Jikun"'
Autor:
Kang, Jikun, Li, Xin Zhe, Chen, Xi, Kazemi, Amirreza, Sun, Qianyi, Chen, Boxing, Li, Dong, He, Xu, He, Quan, Wen, Feng, Hao, Jianye, Yao, Jun
Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical questions. Recent efforts to address this issue have primarily focused on leve
Externí odkaz:
http://arxiv.org/abs/2405.16265
Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. Ho
Externí odkaz:
http://arxiv.org/abs/2401.00907
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and computation. We argue that this inefficiency stems from the forgetting phenomenon, in
Externí odkaz:
http://arxiv.org/abs/2305.16338
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and sat
Externí odkaz:
http://arxiv.org/abs/2303.08003
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designed to control how a DRL agent collects data, which is inspired by how h
Externí odkaz:
http://arxiv.org/abs/2110.03032
Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::068371ebd3bdf8c377b8f1809479487f
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Barrett, Christopher B. (AUTHOR) cbb2@cornell.edu
Publikováno v:
American Journal of Agricultural Economics. Mar2021, Vol. 103 Issue 2, p422-447. 26p. 2 Graphs.
Autor:
Christopher B. Barrett, Tim Benton, Jessica Fanzo, Mario Herrero, Rebecca J. Nelson, Elizabeth Bageant, Edward Buckler, Karen Cooper, Isabella Culotta, Shenggen Fan, Rikin Gandhi, Steven James, Mark Kahn, Laté Lawson-Lartego, Jiali Liu, Quinn Marshall, Daniel Mason-D'Croz, Alexander Mathys, Cynthia Mathys, Veronica Mazariegos-Anastassiou, Alesha Miller, Kamakhya Misra, Andrew Mude, Jianbo Shen, Lindiwe Majele Sibanda, Claire Song, Roy Steiner, Philip Thornton, Stephen Wood
This open access book is the result of an expert panel convened by the Cornell Atkinson Center for Sustainability and Nature Sustainability. The panel tackled the seventeen UN Sustainable Development Goals (SDGs) for 2030 head-on, with respect to the