Results 221 to 230 of about 214,409 (272)

Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies. [PDF]

BMC Genomics
Cao X, Zhu L, Liang X, Zhang S, Sha Q.
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SATrans-Net: Sparse Attention Transformer for EEG-based motor imagery decoding. [PDF]

Sci Rep
Miao T, Sha L, Huang K, Li Y, Liu B.
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Manifold transform by recurrent cortical circuit enhances robust encoding of familiar stimuli. [PDF]

PLoS Comput Biol
Wang W, Niu X, Liang L, Lee TS.
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A bi-channel aided stitching of atomic force microscopy images. [PDF]

Sci Rep
Zhao H, Millan-Solsona R, Checa M, Brown SR, Morrell-Falvey JL, Collins L, Biswas A.   +6 more
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Behavioral Time Scale Synaptic Plasticity (BTSP) endows Hyperdimensional Computing with brain-like information retrieval flexibility

Maass W, Yu C, Wu Y, Wang A.
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Discrete Sparse Coding

Neural Computation, 2017
Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions.
Exarchakis, Georgios, Lücke, Jörg
openaire   +3 more sources

Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework.
Shenghua, Gao, Ivor Wai-Hung, Tsang, Liang-Tien, Chia   +2 more
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