Results 101 to 110 of about 332,886 (299)

A reaction network scheme for hidden Markov model parameter learning. [PDF]

J R Soc Interface, 2023
Wiuf C, Behera A, Singh A, Gopalkrishnan M.   +3 more
europepmc   +1 more source

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

A cooperative multi-agent reinforcement learning algorithm based on dynamic self-selection parameters sharing

智能科学与技术学报, 2022
In multi-agent reinforcement learning, parameter sharing can effectively alleviate the inefficiency of learning caused by non-stationarity.However, maintaining the same policy forall agents during learning may have detrimental effects.To solve this ...
Han WANG, Yang YU, Yuan JIANG
doaj  

Structural Changes in Nonlocal Denoising Models Arising Through Bi-Level Parameter Learning. [PDF]

Appl Math Optim, 2023
Davoli E, Ferreira R, Kreisbeck C, Schönberger H.   +3 more
europepmc   +1 more source

Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou, Marc Jaskir, Julian Gal, Yombe Fonkeu, Christos Davatzikos, MacLean P. Nasrallah, Jose Garcia, Rubo Xing, Stephen J. Bagley, Arati Desai, Richard E. Phillips, Steven Tobochnik, Colin A. Ellis, James J. Gugger, Nishant Sinha, Joel M. Stein, Kathryn A. Davis   +16 more
wiley   +1 more source

Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting

Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge ...
Dong, Zheng, Deng, Jinliang, Song, Xuan, Jiang, Renhe, Gao, Haotian, Liu, Hangchen, Wen, Qingsong   +6 more
core   +3 more sources

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

Learning as filtering: Implications for spike-based plasticity

PLoS Computational Biology, 2022
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters.
Jannes Jegminat, Simone Carlo Surace, Jean-Pascal Pfister   +2 more
doaj  

From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. [PDF]

Nat Commun, 2021
Tsai WP, Feng D, Pan M, Beck H, Lawson K, Yang Y, Liu J, Shen C.   +7 more
europepmc   +1 more source

Parameter-Free Spectral Kernel Learning

CoRR, 2012
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine manifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel.
Qi Mao 0001, Ivor W. Tsang
openaire   +3 more sources