Results 241 to 250 of about 1,409,223 (260)
ASSAFormer: a sensor data-based approach to human activity recognition. [PDF]
Sci RepZeng J, Peng B, Chen C.
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Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies. [PDF]
BMC GenomicsCao X, Zhu L, Liang X, Zhang S, Sha Q.
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Multi-wavelength graph convolutional network for high-performance sparse multispectral optoacoustic tomography. [PDF]
PhotoacousticsLu M, Wang J, Peng J, Li B, Liu X.
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SSRN Electronic Journal, 2023
Graduate Institute of International and Development Studies Working Paper ; no.
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Graduate Institute of International and Development Studies Working Paper ; no.
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INFORMS Journal on Computing, 2021
We consider the problem of best [Formula: see text]-subset convex regression using [Formula: see text] observations in [Formula: see text] variables. For the case without sparsity, we develop a scalable algorithm for obtaining high quality solutions in practical times that compare favorably with other state of the art methods.
Dimitris Bertsimas, Nishanth Mundru
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We consider the problem of best [Formula: see text]-subset convex regression using [Formula: see text] observations in [Formula: see text] variables. For the case without sparsity, we develop a scalable algorithm for obtaining high quality solutions in practical times that compare favorably with other state of the art methods.
Dimitris Bertsimas, Nishanth Mundru
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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.
Exarchakis, Georgios, Lücke, Jörg
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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.
Exarchakis, Georgios, Lücke, Jörg
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Approximate Inverse Preconditioners via Sparse-Sparse Iterations
SIAM Journal on Scientific Computing, 1998The authors consider iteration methods for finding approximate inverse preconditioners \(M\) to the inverse \(A^{-1}\) of a given \((n\times n)\)-matrix \(A\). The iterative methods aim at the minimization of the functional \[ F(M):= \| I- AM\|^2_F= \sum^n_{j=1}\| e_j- Am_j\|^2_2 \] on the space of all \((n\times n)\)-matrices, where \(\|\cdot\|_F ...
Chow, Edmond, Saad, Yousef
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Neural Computation, 2019
It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies.
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It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies.
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IEEE Transactions on Image Processing, 2016
In this paper, we propose a novel tracking framework based on a sparse and discriminative hashing method. Different from the previous work, we treat object tracking as an approximate nearest neighbor searching process in a binary space. Using the hash functions, the target templates and the candidates can be projected into the Hamming space ...
Lihe, Zhang +3 more
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In this paper, we propose a novel tracking framework based on a sparse and discriminative hashing method. Different from the previous work, we treat object tracking as an approximate nearest neighbor searching process in a binary space. Using the hash functions, the target templates and the candidates can be projected into the Hamming space ...
Lihe, Zhang +3 more
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