Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Nam, Andrew"'
Autor:
Nam, Andrew, Elmoznino, Eric, Malkin, Nikolay, McClelland, James, Bengio, Yoshua, Lajoie, Guillaume
Symbolic systems are powerful frameworks for modeling cognitive processes as they encapsulate the rules and relationships fundamental to many aspects of human reasoning and behavior. Central to these models are systematicity, compositionality, and pr
Externí odkaz:
http://arxiv.org/abs/2310.01807
What can be learned about causality and experimentation from passive data? This question is salient given recent successes of passively-trained language models in interactive domains such as tool use. Passive learning is inherently limited. However,
Externí odkaz:
http://arxiv.org/abs/2305.16183
Out-of-distribution generalization (OODG) is a longstanding challenge for neural networks. This challenge is quite apparent in tasks with well-defined variables and rules, where explicit use of the rules could solve problems independently of the part
Externí odkaz:
http://arxiv.org/abs/2210.03275
Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the re
Externí odkaz:
http://arxiv.org/abs/2210.02615
Autor:
Borchers, Lauren R., Yuan, Justin P., Leong, Josiah K., Jo, Booil, Chahal, Rajpreet, Ryu, Joshua, Nam, Andrew, Coury, Saché M., Gotlib, Ian H.
Publikováno v:
In Biological Psychiatry 1 January 2025 97(1):73-80
Autor:
Nam, Andrew J., McClelland, James L.
Neural networks have long been used to model human intelligence, capturing elements of behavior and cognition, and their neural basis. Recent advancements in deep learning have enabled neural network models to reach and even surpass human levels of i
Externí odkaz:
http://arxiv.org/abs/2107.06994
In this study, we introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives, as well as resource and hard-ware constraints. RA-Auto
Externí odkaz:
http://arxiv.org/abs/2011.00073
Knowledge representation has gained in relevance as data from the ubiquitous digitization of behaviors amass and academia and industry seek methods to understand and reason about the information they encode. Success in this pursuit has emerged with d
Externí odkaz:
http://arxiv.org/abs/1811.07974
Autor:
Khan, Mona, Yoo, Seung-Jun, Clijsters, Marnick, Backaert, Wout, Vanstapel, Arno, Speleman, Kato, Lietaer, Charlotte, Choi, Sumin, Hether, Tyler D., Marcelis, Lukas, Nam, Andrew, Pan, Liuliu, Reeves, Jason W., Van Bulck, Pauline, Zhou, Hai, Bourgeois, Marc, Debaveye, Yves, De Munter, Paul, Gunst, Jan, Jorissen, Mark, Lagrou, Katrien, Lorent, Natalie, Neyrinck, Arne, Peetermans, Marijke, Thal, Dietmar Rudolf, Vandenbriele, Christophe, Wauters, Joost, Mombaerts, Peter, Van Gerven, Laura
Publikováno v:
In Cell 24 November 2021 184(24):5932-5949
Autor:
Koenecke, Allison, Nam, Andrew, Lake, Emily, Nudell, Joe, Quartey, Minnie, Mengesha, Zion, Toups, Connor, Rickford, John R., Jurafsky, Dan, Goel, Sharad
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Apr 01. 117(14), 7684-7689.
Externí odkaz:
https://www.jstor.org/stable/26929712