Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Xu, Haoyin"'
Autor:
Dey, Jayanta, Xu, Haoyin, LeVine, Will, De Silva, Ashwin, Tomita, Tyler M., Geisa, Ali, Chu, Tiffany, Desman, Jacob, Vogelstein, Joshua T.
Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particu
Externí odkaz:
http://arxiv.org/abs/2201.13001
Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, especially on tabular data. However, most of the current implementations only operate in batch mode
Externí odkaz:
http://arxiv.org/abs/2110.08483
Autor:
Dey, Jayanta, Geisa, Ali, Mehta, Ronak, Tomita, Tyler M., Helm, Hayden S., Xu, Haoyin, Eaton, Eric, Dick, Jeffery, Priebe, Carey E., Vogelstein, Joshua T.
Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at on
Externí odkaz:
http://arxiv.org/abs/2109.14501
Autor:
Xu, Haoyin, Kinfu, Kaleab A., LeVine, Will, Panda, Sambit, Dey, Jayanta, Ainsworth, Michael, Peng, Yu-Chung, Kusmanov, Madi, Engert, Florian, White, Christopher M., Vogelstein, Joshua T., Priebe, Carey E.
Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many papers have empirically compared large numbers of classifiers on one o
Externí odkaz:
http://arxiv.org/abs/2108.13637
Autor:
Vogelstein, Joshua T., Dey, Jayanta, Helm, Hayden S., LeVine, Will, Mehta, Ronak D., Tomita, Tyler M., Xu, Haoyin, Geisa, Ali, Wang, Qingyang, van de Ven, Gido M., Gao, Chenyu, Yang, Weiwei, Tower, Bryan, Larson, Jonathan, White, Christopher M., Priebe, Carey E.
In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior
Externí odkaz:
http://arxiv.org/abs/2004.12908
Akademický článek
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Publikováno v:
Proceedings of the Annual Meeting of the Cognitive Science Society, vol 45, iss 45
Despite the universal phenomenon of humor across societies and communities, humor comprehension in second language (L2) speakers is often overlooked. It is unclear whether L2 speakers rely primarily on sociocultural proficiency or linguistic proficie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::b7f7506fe5232336d32f83773b5dadbc
https://escholarship.org/uc/item/50m0z3hf
https://escholarship.org/uc/item/50m0z3hf
Autor:
Dey, Jayanta, Xu, Haoyin, De Silva, Ashwin, LeVine, Will, Tomita, Tyler M., Geisa, Ali, Chu, Tiffany, Desman, Jacob, Vogelstein, Joshua T.
The fight between discriminative versus generative goes deep, in both the study of artificial and natural intelligence. In our view, both camps have complementary values. So, we sought to synergistically combine them. Here, we propose a methodology t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b124cd58ef5ad78f930e9b93548b3afd
Publikováno v:
Proceedings of the Annual Meeting of the Cognitive Science Society, vol 44, iss 44
Humor comprehension is a great challenge for foreign/second language (L2) learners. Previous studies on humor comprehension in L2 speakers have relied only on descriptive approaches or subjective ratings on humor materials. However, no study has quan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::11e8d171121a5f66ba14c2c74d3135d7
https://escholarship.org/uc/item/06v132cc
https://escholarship.org/uc/item/06v132cc
Publikováno v:
Proceedings of the Annual Meeting of the Cognitive Science Society, vol 44, iss 44
The goal of language learning should be to fit in with the language community, and this often requires much more than linguistic knowledge. Although both social wellness in a second language (L2) society and L2 humor comprehension require sophisticat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::a4e24e9fb8515ef3ea371366eb1daa1d
https://escholarship.org/uc/item/0rg79466
https://escholarship.org/uc/item/0rg79466