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of 81
pro vyhledávání: '"Honavar, Vasant G."'
This paper introduces a novel generalized self-imitation learning ($\textbf{GSIL}$) framework, which effectively and efficiently aligns large language models with offline demonstration data. We develop $\textbf{GSIL}$ by deriving a surrogate objectiv
Externí odkaz:
http://arxiv.org/abs/2410.10093
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery. However, current pre-training frameworks are limited to two modalities, and des
Externí odkaz:
http://arxiv.org/abs/2403.08167
Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally no
Externí odkaz:
http://arxiv.org/abs/2403.07179
Autor:
Ren, Weijieying, Honavar, Vasant G
A key challenge in the continual learning setting is to efficiently learn a sequence of tasks without forgetting how to perform previously learned tasks. Many existing approaches to this problem work by either retraining the model on previous tasks o
Externí odkaz:
http://arxiv.org/abs/2401.05667
This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. We utilized 3-level mixed effects negative binomial regression models to estimate the impact of network COVID-19 exposu
Externí odkaz:
http://arxiv.org/abs/2010.01101
Autor:
Roberts, Daniel M., Schade, Margeaux M., Master, Lindsay, Honavar, Vasant G., Nahmod, Nicole G., Chang, Anne-Marie, Gartenberg, Daniel, Buxton, Orfeu M.
Publikováno v:
In Sleep Health: Journal of the National Sleep Foundation October 2023 9(5):596-610
Publikováno v:
In Health and Place September 2022 77
Autor:
Honavar, Vasant G., Yelick, Katherine, Nahrstedt, Klara, Rushmeier, Holly, Rexford, Jennifer, Hill, Mark D., Bradley, Elizabeth, Mynatt, Elizabeth
Progress in many domains increasingly benefits from our ability to view the systems through a computational lens, i.e., using computational abstractions of the domains; and our ability to acquire, share, integrate, and analyze disparate types of data
Externí odkaz:
http://arxiv.org/abs/1707.00599
Akademický článek
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The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural p
Externí odkaz:
http://arxiv.org/abs/1604.02006