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Data Compression

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Machine learning at the interface of structural health monitoring and non-destructive evaluation.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities...

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

NMR in biomedicine
Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achieve high spatio-temporal resolution and clinically acceptable image quality in patients with arrhythmia and/or dyspnea. However, its lengthy image reco...

A combined HMM-PCNN model in the contourlet domain for image data compression.

PloS one
Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data s...

Compressing 3DCNNs based on tensor train decomposition.

Neural networks : the official journal of the International Neural Network Society
Three-dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger th...

Research and Verification of Convolutional Neural Network Lightweight in BCI.

Computational and mathematical methods in medicine
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convo...

Block-term tensor neural networks.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good performance, the huge number of parameters in DNNs brings challenges to ...

Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.

Genes
Single-cell RNA-seq (scRNA-seq) is quite prevalent in studying transcriptomes, but it suffers from excessive zeros, some of which are true, but others are false. False zeros, which can be seen as missing data, obstruct the downstream analysis of sing...

Multi-way backpropagation for training compact deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet (He et al., 2016), we are able to train very deep CNNs as the gradient vanishing issue has been largely addressed by the introduction of skip con...

Structured pruning of recurrent neural networks through neuron selection.

Neural networks : the official journal of the International Neural Network Society
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it difficult for their deployment on edge devices. A practically effective...

Application of Deep Compression Technique in Spiking Neural Network Chip.

IEEE transactions on biomedical circuits and systems
In this paper, a reconfigurable and scalable spiking neural network processor, containing 192 neurons and 6144 synapses, is developed. By using deep compression technique in spiking neural network chip, the amount of physical synapses can be reduced ...