Zobrazeno 1 - 10
of 262
pro vyhledávání: '"Reyes,Omar"'
We present MathDSL, a Domain-Specific Language (DSL) for mathematical equation solving, which, when deployed in program synthesis models, outperforms state-of-the-art reinforcement-learning-based methods. We also introduce a quantitative metric for m
Externí odkaz:
http://arxiv.org/abs/2409.17490
Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), and numerous studies have identified an association between comorbid anxiety and resistance to pharmacological depression treatment. However, less is known reg
Externí odkaz:
http://arxiv.org/abs/2409.11183
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by induci
Externí odkaz:
http://arxiv.org/abs/2409.09359
Digital phenotyping in mental health often consists of collecting behavioral and experience-based information through sensory and self-reported data from devices such as smartphones. Such rich and comprehensive data could be used to develop insights
Externí odkaz:
http://arxiv.org/abs/2312.01216
Clinical practice in psychiatry is burdened with the increased demand for healthcare services and the scarce resources available. New paradigms of health data powered with machine learning techniques could open the possibility to improve clinical wor
Externí odkaz:
http://arxiv.org/abs/2306.03980
Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thus making them more likely to be found. In this paper, we propose Learn
Externí odkaz:
http://arxiv.org/abs/2211.08671
Autor:
Sun, Jennifer J., Tjandrasuwita, Megan, Sehgal, Atharva, Solar-Lezama, Armando, Chaudhuri, Swarat, Yue, Yisong, Costilla-Reyes, Omar
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 constrain
Externí odkaz:
http://arxiv.org/abs/2210.05050
Autor:
Torres-Pérez, Wesley X.1 wesley.torres@upr.edu, Pérez-Reyes, Omar1 wesley.torres@upr.edu
Publikováno v:
Hydrobiology. Sep2024, Vol. 3 Issue 3, p149-158. 10p.
Autor:
Greenberg, Jennifer L., Weingarden, Hilary, Hoeppner, Susanne S., Berger-Gutierrez, Rebecca M., Klare, Dalton, Snorrason, Ivar, Costilla-Reyes, Omar, Talbot, Morgan, Daniel, Katharine E., Vanderkruik, Rachel C., Solar-Lezama, Armando, Harrison, Oliver, Wilhelm, Sabine
Publikováno v:
In Journal of Affective Disorders 15 June 2024 355:106-114