Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Arthur Choi"'
Autor:
Glenn J Fernandes, Arthur Choi, Jacob Michael Schauer, Angela F Pfammatter, Bonnie J Spring, Adnan Darwiche, Nabil I Alshurafa
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e42047 (2023)
BackgroundPredicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatmen
Externí odkaz:
https://doaj.org/article/478238e9e6774c62bd15b6110ebe795b
Autor:
Richard Borowski, null Arthur Choi
Publikováno v:
The International FLAIRS Conference Proceedings. 36
A neuron with binary inputs and a binary output represents a Boolean function. Our goal is to extract this Boolean function into a tractable representation that will facilitate the explanation and formal verification of a neuron's behavior. Unfortuna
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 26:842-849
The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are charac
Autor:
Glenn J Fernandes, Arthur Choi, Jacob Michael Schauer, Angela F Pfammater, Bonnie J Spring, Adnan Darwiche, Nabil I Alshurafa
BACKGROUND Predicting the likelihood of success of weight loss in interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to trea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86f259981b27a81e23ff13caba657fc5
https://doi.org/10.2196/preprints.42047
https://doi.org/10.2196/preprints.42047
Autor:
Arthur Choisez, Seiichiro Ishihara, Takuro Ishii, Yidan Xu, Sepideh D. Firouzjah, Hisashi Haga, Ryoichi Nagatomi, Joji Kusuyama
Publikováno v:
Journal of Lipid Research, Vol 65, Iss 9, Pp 100620- (2024)
Adipose tissue remodeling and plasticity are dynamically regulated by the coordinated functions of adipocytes, macrophages, and endothelial cells and extracellular matrix (ECM) that provides stiffness networks in adipose tissue component cells. Infla
Externí odkaz:
https://doaj.org/article/7ff8834432584bc0906226e579b5282a
Publikováno v:
KR
We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs). Obtaining this function as an OBDD/SDD facilitates the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39776fa0ac67b2868cc2e72426ce6c61
http://arxiv.org/abs/2004.02082
http://arxiv.org/abs/2004.02082
Publikováno v:
International Journal of Approximate Reasoning. 92:363-375
The space of Bayesian network structures is prohibitively large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale
Publikováno v:
Artificial Intelligence. 244:239-257
We propose a principled approach for learning parameters in Bayesian networks from incomplete datasets, where the examples of a dataset are subject to equivalence constraints. These equivalence constraints arise from datasets where examples are tied
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030242572
SAT
SAT
We consider the problem of verifying the behavior of binarized neural networks on some input region. We propose an Angluin-style learning algorithm to compile a neural network on a given region into an Ordered Binary Decision Diagram (OBDD), using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5dac3ed58c29fb3af2eb99b31fc5afe
https://doi.org/10.1007/978-3-030-24258-9_25
https://doi.org/10.1007/978-3-030-24258-9_25
A neural network computes a function. A central property of neural networks is that they are “universal approximators:” for a given continuous function, there exists a neural network that can approximate it arbitrarily well, given enough neurons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2f4b2d66d93b82d83fa7cdc4a26478b
http://arxiv.org/abs/1812.08957
http://arxiv.org/abs/1812.08957