Zobrazeno 1 - 10
of 2 741
pro vyhledávání: '"Moore, Jason A."'
Autor:
Shang, Tianqi, Yang, Shu, He, Weiqing, Zhai, Tianhua, Li, Dawei, Hou, Bojian, Chen, Tianlong, Moore, Jason H., Ritchie, Marylyn D., Shen, Li
Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relatio
Externí odkaz:
http://arxiv.org/abs/2410.09080
Lexicase selection is a successful parent selection method in genetic programming that has outperformed other methods across multiple benchmark suites. Unlike other selection methods that require explicit parameters to function, such as tournament si
Externí odkaz:
http://arxiv.org/abs/2407.15056
Feature selection in Knowledge Graphs (KGs) are increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature
Externí odkaz:
http://arxiv.org/abs/2406.14864
Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages t
Externí odkaz:
http://arxiv.org/abs/2406.12006
Autor:
Yu, Jun, Dai, Yutong, Liu, Xiaokang, Huang, Jin, Shen, Yishan, Zhang, Ke, Zhou, Rong, Adhikarla, Eashan, Ye, Wenxuan, Liu, Yixin, Kong, Zhaoming, Zhang, Kai, Yin, Yilong, Namboodiri, Vinod, Davison, Brian D., Moore, Jason H., Chen, Yong
MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a suite of benefits that enhance both the training process and the infere
Externí odkaz:
http://arxiv.org/abs/2404.18961
Autor:
Sipper, Moshe, Moore, Jason H.
Publikováno v:
Genetic Programming and Evolvable Machines (2020) 21:169-179
The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We
Externí odkaz:
http://arxiv.org/abs/2402.00425
Publikováno v:
J. Romero et al. (Eds.), EvoMUSART 2020, LNCS 12103, pp. 165-178, 2020
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstrate that the OMNIREP framework can be successfully applied within the
Externí odkaz:
http://arxiv.org/abs/2401.11167
Publikováno v:
W. Banzhaf et al. (eds.), Genetic Programming Theory and Practice XVII, Genetic and Evolutionary Computation, 2020
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation. Herein, we present two new forms of coevolutionary algorithms, which we have recently designed
Externí odkaz:
http://arxiv.org/abs/2401.10515
Autor:
Moore, Jason, Genkin, Alexander, Tournoy, Magnus, Pughe-Sanford, Joshua, van Steveninck, Rob R. de Ruyter, Chklovskii, Dmitri B.
In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends the current normative mod
Externí odkaz:
http://arxiv.org/abs/2401.01489
Autor:
Huang, Yu-Ning, Love, Michael I., Ronkowski, Cynthia Flaire, Deshpande, Dhrithi, Schriml, Lynn M., Wong-Beringer, Annie, Mons, Barend, Corbett-Detig, Russell, Hunter, Christopher I, Moore, Jason H., Garmire, Lana X., Reddy, T. B. K., Hide, Winston A., Butte, Atul J., Robinson, Mark D., Mangul, Serghei
Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effecti
Externí odkaz:
http://arxiv.org/abs/2401.02965