Results 51 to 60 of about 1,415,945 (160)
Scalable Low Power Accelerator for Sparse Recurrent Neural Network
天地一体化信息网络, 2023 The use of edge computing devices in bank outlets for passenger flow analysis, security protection, risk prevention and control is increasingly widespread, among which the performance and power consumption of AI reasoning chips have become a very ...
Panshi JIN +5 more
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Sparse thalamocortical convergence
Current Biology, 2021 How many thalamic neurons converge onto a cortical cell? This is an important question, because the organization of thalamocortical projections can influence the cortical architecture.1,2 Here, we estimate the degree of thalamocortical convergence in primary visual cortex by taking advantage of the cortical expansion-neurons within a restricted volume ...
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Henneberg constructions and covers of cone-Laman graphs [PDF]
, 2012 We give Henneberg-type constructions for three families of sparse colored graphs arising in the rigidity theory of periodic and other forced symmetric frameworks.
Theran, Louis
core
Jump-sparse and sparse recovery using Potts functionals
, 2014 We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals.
Demaret, Laurent +2 more
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Stabilized Sparse Online Learning for Sparse Data
, 2016 45 pages, 4 ...
Ma, Yuting, Zheng, Tian
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Sparse Quantile Regression [PDF]
Journal of Econometrics, 2020 We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation ...
Lee, Sokbae (Simon), Chen, Le-Yu
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Journal of Applied and Computational Topology, 2019 25 ...
Brun, Morten, Blaser, Nello
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Dictionary-Based Low-Rank Approximations and the Mixed Sparse Coding Problem
Frontiers in Applied Mathematics and Statistics, 2022 Constrained tensor and matrix factorization models allow to extract interpretable patterns from multiway data. Therefore crafting efficient algorithms for constrained low-rank approximations is nowadays an important research topic.
Jeremy E. Cohen
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Domain Decomposition Based High Performance Parallel Computing\ud [PDF]
, 2009 The study deals with the parallelization of finite element based Navier-Stokes codes using domain decomposition and state-ofart sparse direct solvers. There has been significant improvement in the performance of sparse direct solvers.
Khaitan, Siddhartha +1 more
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EvAn: Neuromorphic Event-Based Sparse Anomaly Detection
Frontiers in Neuroscience, 2021 Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range ...
Lakshmi Annamalai +3 more
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