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Volume 32, Issue 8

August 2020

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ISSN 0899-7667

EISSN 1530-888X

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Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective

Shun-ichi Amari

Neural Computation (2020) 32 (8): 1431–1447.

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Inference of a Mesoscopic Population Model from Population Spike Trains

Alexandre René, André Longtin, Jakob H. Macke

Neural Computation (2020) 32 (8): 1448–1498.

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Stochastic Multichannel Ranking with Brain Dynamics Preferences

Yuangang Pan, Ivor W. Tsang, Avinash K. Singh, Chin-Teng Lin, Masashi Sugiyama

Neural Computation (2020) 32 (8): 1499–1530.

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A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization

Xinqi Li, Jun Wang, Sam Kwong

Neural Computation (2020) 32 (8): 1531–1562.

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On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning

Illia Horenko

Neural Computation (2020) 32 (8): 1563–1579.

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Theory and Algorithms for Shapelet-Based Multiple-Instance Learning

Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai ...

Neural Computation (2020) 32 (8): 1580–1613.

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Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective

Article

Inference of a Mesoscopic Population Model from Population Spike Trains

Letters

Stochastic Multichannel Ranking with Brain Dynamics Preferences

A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization

On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning

Theory and Algorithms for Shapelet-Based Multiple-Instance Learning

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Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective

Article

Inference of a Mesoscopic Population Model from Population Spike Trains

Letters

Stochastic Multichannel Ranking with Brain Dynamics Preferences

A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization

On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning

Theory and Algorithms for Shapelet-Based Multiple-Instance Learning

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A product of The MIT Press

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