Zobrazeno 1 - 10
of 221 952
pro vyhledávání: '"LEVINE AS"'
Autor:
Levine, Benjamin, Sánchez, Javier, Chang, Chihway, von der Linden, Anja, Collins, Eboni, Gawiser, Eric, Krzyżańska, Katarzyna, Leistedt, Boris, Collaboration, The LSST Dark Energy Science
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps,
Externí odkaz:
http://arxiv.org/abs/2411.14564
Autor:
Lu, Yiwen, Tong, Jiayi, Lei, Yuqing, Sutton, Alex J., Chu, Haitao, Levine, Lisa D., Lumley, Thomas, Asch, David A., Duan, Rui, Schmid, Christopher H., Chen, Yong
We introduce OrigamiPlot, an open-source R package and Shiny web application designed to enhance the visualization of multivariate data. This package implements the origami plot, a novel visualization technique proposed by Duan et al. in 2023, which
Externí odkaz:
http://arxiv.org/abs/2411.12674
Autor:
Kang, Katie, Setlur, Amrith, Ghosh, Dibya, Steinhardt, Jacob, Tomlin, Claire, Levine, Sergey, Kumar, Aviral
Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream
Externí odkaz:
http://arxiv.org/abs/2411.07681
Recent progress on large language models (LLMs) has enabled dialogue agents to generate highly naturalistic and plausible text. However, current LLM language generation focuses on responding accurately to questions and requests with a single effectiv
Externí odkaz:
http://arxiv.org/abs/2411.05194
Value-based reinforcement learning (RL) can in principle learn effective policies for a wide range of multi-turn problems, from games to dialogue to robotic control, including via offline RL from static previously collected datasets. However, despite
Externí odkaz:
http://arxiv.org/abs/2411.05193
Autor:
Glazer, Elliot, Erdil, Ege, Besiroglu, Tamay, Chicharro, Diego, Chen, Evan, Gunning, Alex, Olsson, Caroline Falkman, Denain, Jean-Stanislas, Ho, Anson, Santos, Emily de Oliveira, Järviniemi, Olli, Barnett, Matthew, Sandler, Robert, Vrzala, Matej, Sevilla, Jaime, Ren, Qiuyu, Pratt, Elizabeth, Levine, Lionel, Barkley, Grant, Stewart, Natalie, Grechuk, Bogdan, Grechuk, Tetiana, Enugandla, Shreepranav Varma, Wildon, Mark
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensiv
Externí odkaz:
http://arxiv.org/abs/2411.04872
Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to
Externí odkaz:
http://arxiv.org/abs/2411.02623
Autor:
Johnson, Samuel G. B., Karimi, Amir-Hossein, Bengio, Yoshua, Chater, Nick, Gerstenberg, Tobias, Larson, Kate, Levine, Sydney, Mitchell, Melanie, Rahwan, Iyad, Schölkopf, Bernhard, Grossmann, Igor
Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack o
Externí odkaz:
http://arxiv.org/abs/2411.02478
Autor:
Liu, Yang Janet, Aoyama, Tatsuya, Scivetti, Wesley, Zhu, Yilun, Behzad, Shabnam, Levine, Lauren Elizabeth, Lin, Jessica, Tiwari, Devika, Zeldes, Amir
Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by no
Externí odkaz:
http://arxiv.org/abs/2411.00491
Autor:
Black, Kevin, Brown, Noah, Driess, Danny, Esmail, Adnan, Equi, Michael, Finn, Chelsea, Fusai, Niccolo, Groom, Lachy, Hausman, Karol, Ichter, Brian, Jakubczak, Szymon, Jones, Tim, Ke, Liyiming, Levine, Sergey, Li-Bell, Adrian, Mothukuri, Mohith, Nair, Suraj, Pertsch, Karl, Shi, Lucy Xiaoyang, Tanner, James, Vuong, Quan, Walling, Anna, Wang, Haohuan, Zhilinsky, Ury
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of g
Externí odkaz:
http://arxiv.org/abs/2410.24164