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Multi-Level Variable Selection Using a BART-Enhanced Mixed-Effects Framework. [PDF]
Stat MedZhang K +5 more
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Risk of Inflammatory Bowel Disease After Anti-CD20 Treatment in Multiple Sclerosis.
JAMA NeurolKallin S +5 more
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On Sparse Regression LDPC Codes
2023 IEEE International Symposium on Information Theory (ISIT), 2023 Belief propagation applied to iterative decoding and sparse recovery through approximate message passing (AMP) are two research areas that have seen monumental progress in recent decades.
Krishna Narayanan +2 more
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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.
Georgios Exarchakis, Jörg Lücke
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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.
Georgios Exarchakis, Jörg Lücke
openaire +3 more sources
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Sparse 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 +2 more
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2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
Sparse coding is a widely involved technique in computer vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
Tiezheng Ge, Kaiming He, Jian Sun 0001
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Sparse coding is a widely involved technique in computer vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
Tiezheng Ge, Kaiming He, Jian Sun 0001
openaire +2 more sources