Results 11 to 20 of about 214,409 (272)
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 ...
Romano, Yaniv +7 more
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Neural network detector with sparse codes for spin transfer torque magnetic random access memory
Cogent Engineering, 2023 This paper presents leveraging the neural network detector to improve the performance of a spin transfer torque magnetic random-access memory (STT-MRAM), where the sparse coding scheme is also applied to protect the user data for the asymmetric write ...
Chi Dinh Nguyen
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On the Logical Error Rate of Sparse Quantum Codes
IEEE Transactions on Quantum Engineering, 2022 The quantum paradigm presents a phenomenon known as degeneracy that can potentially improve the performance of quantum error correcting codes. However, the effects of this mechanism are sometimes ignored when evaluating the performance of sparse quantum ...
Patricio Fuentes +3 more
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IEEE Access, 2023 Spin-torque-transfer magnetic random access memory (STT-MRAM) has recently emerged as a promising technology to replace dynamic random access memory (DRAM).
Thien An Nguyen, Jaejin Lee
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On the Design of 7/9-Rate Sparse Code for Spin-Torque Transfer Magnetic Random Access Memory
IEEE Access, 2021 A design of 7/9-rate sparse code for spin-torque transfer magnetic random access memory (STT-MRAM) is proposed in this work. The STT-MRAM using spin-polarized current through magnetic tunnel junction (MTJ) to write data is one of the most promising ...
Chi Dinh Nguyen
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Sparse-Coding Variational Autoencoders [PDF]
Neural Computation, 2018 Abstract The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) computing the neural response to an image patch required minimizing a nonlinear ...
Victor Geadah +4 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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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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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, sparse-graph codes, and polar codes. These
Venkataramanan, Ramji +2 more
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