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pro vyhledávání: '"Dattner, Itai"'
Autor:
Shulman, David, Israeli, Assaf, Botnaro, Yael, Margalit, Ori, Tamir, Oved, Naschitz, Shaul, Gamrasni, Dan, Shir, Ofer M., Dattner, Itai
We present an innovative approach leveraging Physics-Guided Neural Networks (PGNNs) for enhancing agricultural quality assessments. Central to our methodology is the application of physics-guided inverse regression, a technique that significantly imp
Externí odkaz:
http://arxiv.org/abs/2403.08653
Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nu
Externí odkaz:
http://arxiv.org/abs/2308.16438
Autor:
Dattner, Itai
This study introduces a novel mechanistic modeling and statistical framework for analyzing motion energy dynamics within psychotherapy sessions. We transform raw motion energy data into an interpretable narrative of therapist-patient interactions, th
Externí odkaz:
http://arxiv.org/abs/2307.10992
Multi-Class Processing Networks describe a set of servers that perform multiple classes of jobs on different items. A useful and tractable way to find an optimal control for such a network is to approximate it by a fluid model, resulting in a Separat
Externí odkaz:
http://arxiv.org/abs/2207.04472
Publikováno v:
In Journal of Sound and Vibration 10 November 2024 590
Nonlinear dynamic models are widely used for characterizing functional forms of processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected v
Externí odkaz:
http://arxiv.org/abs/1908.03717
Understanding age-group dynamics of infectious diseases is a fundamental issue for both scientific study and policymaking. Age-structure epidemic models were developed in order to study and improve our understanding of these dynamics. By fitting the
Externí odkaz:
http://arxiv.org/abs/1907.03441
Akademický článek
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Autor:
Yaari, Rami, Dattner, Itai
In this paper we describe simode: Separable Integral Matching for Ordinary Differential Equations. The statistical methodologies applied in the package focus on several minimization procedures of an integral-matching criterion function, taking advant
Externí odkaz:
http://arxiv.org/abs/1807.04202
In this paper we present the GFP-growth (Guided FP-growth) algorithm, a novel method for multitude-targeted mining: finding the count of a given large list of itemsets in large data. The GFP-growth algorithm is designed to focus on the specific multi
Externí odkaz:
http://arxiv.org/abs/1803.06632