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Neurosymbolic Programming for Science

Sun, Jennifer J. and Tjandrasuwita, Megan and Sehgal, Atharva and Solar-Lezama, Armando and Chaudhuri, Swarat and Yue, Yisong and Costilla-Reyes, Omar (2022) Neurosymbolic Programming for Science. . (Unpublished)

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Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Sun, Jennifer J.0000-0002-0906-6589
Solar-Lezama, Armando0000-0001-7604-8252
Chaudhuri, Swarat0000-0002-6859-1391
Yue, Yisong0000-0001-9127-1989
Costilla-Reyes, Omar0000-0001-8331-7262
Additional Information:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). This project was supported by the National Science Foundation under Grant #1918839 “Understanding the World Through Code”
Funding AgencyGrant Number
Record Number:CaltechAUTHORS:20221219-234119032
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118473
Deposited By: George Porter
Deposited On:21 Dec 2022 19:34
Last Modified:02 Jun 2023 01:29

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