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This paper proposes the concept of kernel chains, finely characterizing inter-layer alignment features of pruned models at different pruning rates, ...
Sep 12, 2024 · This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related ...
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Feb 29, 2024 · Model pruning is a technique to remove unimportant parameters from neural networks, enhancing efficiency without significantly compromising performance.
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May 24, 2019 · Here is a way to prune a layer (a weight matrix) of your neural network. What the method essentially does is selects the k% smallest weights (elements of the ...
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May 6, 2024 · Pruning is a popular technique for compressing neural networks through the elimination of weights, yielding sparse networks (LeCun et al., 1989; ...
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Jun 26, 2020 · The catch about pruning is that you can only increase efficiency, speed, etc. after training is done. You still have to train with the full size network.
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This paper presents a synchronous weight quantization-compression (SWQC) technique to compress the weights of low-bit quantized neural network (QNN).
Nov 12, 2020 · The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during ...
We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit ...
Jun 26, 2023 · We propose RePurpose, a layer-wise model restructuring and pruning technique that guarantees the performance of the overall parallelized model.
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