Results 11 to 20 of about 864,269 (299)
Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article/Sparse_codingMammalian brains consist of billions of neurons, each capable of independent electrical activity.
Endres, D M, Foldiak, P
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Quantum Machine Intelligence, 2022 Abstract The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-hard in general, and it is therefore always approached with an approximation method, such as lasso or orthogonal matching pursuit, thus trading off accuracy ...
Yaniv Romano +7 more
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IEEE Access, 2023 In non-volatile random-access memory (RAM) technologies, the spin-torque transfer magnetic random-access memory (STT-MRAM) is a promising candidate. STT-MRAM has attracted attention owing to its advantages, such as a high density, high endurance, and ...
Thien An Nguyen, Jaejin Lee
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Foundations and Trends® in Communications and Information Theory, 2019 Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparsegraph codes, and polar codes.
Ramji Venkataramanan +2 more
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Sparse Coding and Autoencoders [PDF]
2018 IEEE International Symposium on Information Theory (ISIT), 2018 In this new version of the paper with a small change in the distributional assumptions we are actually able to prove the asymptotic criticality of a neighbourhood of the ground truth dictionary for even just the standard squared loss of the ReLU autoencoder (unlike the regularized loss in the older version)
Akshay Rangamani +6 more
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Synthesis of a comprehensive population code for contextual features in the awake sensory cortex
eLife, 2021 How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features.
Evan H Lyall +5 more
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Mathematics, 2022 Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC ...
Bing Zhang, Jizhong Liu
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Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
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Class-specific sparse codes for representing activities [PDF]
, 2015 In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state ...
Denman, Simon +7 more
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IEEE Transactions on Information Theory, 2014 51 pages, 12 figures, submitted to IEEE Transactions on Information ...
Shenghao Yang 0001, Raymond W. Yeung
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