Results 51 to 60 of about 489,349 (320)
Sparse PCA from Sparse Linear Regression
CoRR, 2018 To appear in NeurIPS ...
Bresler, Guy +2 more
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Factors of Sparse Polynomials are Sparse
CoRR, 2014 This paper was removed due to an error in the proof (Claim 4.12 as stated is not true)
Dvir, Zeev, de Oliveira, Rafael Mendes
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Stabilized Sparse Online Learning for Sparse Data
J. Mach. Learn. Res., 2016 45 pages, 4 ...
Yuting Ma, Tian Zheng
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SSRN Electronic Journal, 2014 Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegrating vectors. In this paper, we provide a sparse estimator of the cointegrating vectors.
Wilms, Ines, Croux, Christophe
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Adaptive sparse tiling for sparse matrix multiplication [PDF]
Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming, 2019 Tiling is a key technique for data locality optimization and is widely used in high-performance implementations of dense matrix-matrix multiplication for multicore/manycore CPUs and GPUs. However, the irregular and matrix-dependent data access pattern of sparse matrix multiplication makes it challenging to use tiling to enhance data reuse.
Changwan Hong +4 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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Scottish Journal of Political Economy, 2023 ABSTRACT Forecasting economic activity during institutional collapse requires nowcasts derived exclusively from alternative data sources. Such sources are abundant yet theoretically unanchored and potentially weakly informative. This study examines whether sparse supervised dimension reduction extracts reliable signals in a context ...
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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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