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Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
International Conference on Learning Representations (ICLR), 2014
19 December 2014
V. Lebedev
Yaroslav Ganin
M. Rakhuba
Ivan Oseledets
Victor Lempitsky
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Papers citing
"Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition"
50 / 412 papers shown
FedNILM: Applying Federated Learning to NILM Applications at the Edge
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Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
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RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging
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Revisiting Dynamic Convolution via Matrix Decomposition
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Manifold Regularized Dynamic Network Pruning
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VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
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Block-term Tensor Neural Networks
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Transform Quantization for CNN (Convolutional Neural Network) Compression
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Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network
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Tensor Low-Rank Reconstruction for Semantic Segmentation
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Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models
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T-Basis: a Compact Representation for Neural Networks
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307
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152
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MARS: Masked Automatic Ranks Selection in Tensor Decompositions
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