Results 51 to 60 of about 502,069 (324)
Memorized sparse backpropagation [PDF]
Neurocomputing, 2020 Accepted to ...
Zhiyuan Zhang 0001 +4 more
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Sparse Activity and Sparse Connectivity in Supervised Learning
J. Mach. Learn. Res., 2013 Sparseness is a useful regularizer for learning in a wide range of applications, in particular in neural networks. This paper proposes a model targeted at classification tasks, where sparse activity and sparse connectivity are used to enhance classification capabilities.
Markus Thom, Günther Palm
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Sparse PCA from Sparse Linear Regression
CoRR, 2018 To appear in NeurIPS ...
Bresler, Guy +2 more
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Sparse Ordinal Logistic Regression and Its Application to Brain Decoding
Frontiers in Neuroinformatics, 2018 Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity.
Emi Satake +4 more
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IEEE Access, 2023 Recently, an absolute value inequalities discriminant analysis criterion with robustness and sparseness for supervised dimensionality reduction was studied.
Chun-Na Li +4 more
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Characterizing the sparseness of neural codes.
, 2001 It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different ways--firstly to describe codes in which few neurons are active at any time ('population ...
B. Willmore, D.J. Tolhurst
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Back-Propagation Learning in Deep Spike-By-Spike Networks
Frontiers in Computational Neuroscience, 2019 Artificial neural networks (ANNs) are important building blocks in technical applications. They rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real brains. We propose
David Rotermund, Klaus R. Pawelzik
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Cancer Informatics, 2022 Identifying individual mechanisms involved in complex diseases, such as cancer, is essential for precision medicine. Their characterization is particularly challenging due to the unknown relationships of high-dimensional omics data and their inter ...
Sarah-Laure Rincourt +2 more
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JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM
Sensors, 2021 Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on ...
Lu Yin, Pengcheng Ma, Zhongliang Deng
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Evaluation of a library of loxP variants with a wide range of recombination efficiencies by Cre.
PLoS ONE, 2022 Sparse labeling of individual cells is an important approach in neuroscience and many other fields of research. Various methods have been developed to sparsely label only a small population of cells; however, there is no simple and reproducible strategy ...
Yuji Yamauchi +4 more
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