Compressing convolutional neural networks with hierarchical Tucker-2 decomposition

Fast Hierarchical Tucker Decomposition with Single-Mode Preservation and Tensor Subspace Analysis for Feature Extraction from Augmented Multimodal Data

(2021) Krzysztof Fonał et al.   NEUROCOMPUTING

HRel: Filter pruning based on High Relevance between activation maps and class labels

(2021) C.H. Sarvani et al.   NEURAL NETWORKS

Memory devices and applications for in-memory computing

(2020) Abu Sebastian et al.   Nature Nanotechnology

AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

(2020) Jian-Hao Luo et al.   PATTERN RECOGNITION

Hybrid tensor decomposition in neural network compression

(2020) Bijiao Wu et al.   NEURAL NETWORKS

Deep Learning With Edge Computing: A Review

(2019) Jiasi Chen et al.   PROCEEDINGS OF THE IEEE

Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

(2019) Shaohui Lin et al.   IEEE Transactions on Neural Networks and Learning Systems

Deep compression of convolutional neural networks with low-rank approximation

(2018) Marcella Astrid et al.   ETRI JOURNAL

Accelerating Very Deep Convolutional Networks for Classification and Detection

(2016) Xiangyu Zhang et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Deep learning

(2015) Yann LeCun et al.   NATURE

Tensor-Train Decomposition

(2011) I. V. Oseledets   SIAM JOURNAL ON SCIENTIFIC COMPUTING

A New Scheme for the Tensor Representation

(2009) W. Hackbusch et al.   JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS

Tensor Decompositions and Applications

(2009) Tamara G. Kolda et al.   SIAM REVIEW