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Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments.
The difficulty of training sparse neural networks
Utku Evci, Fabian Pedregosa, Aidan Gomez, and Erich Elsen · 1906
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Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 1911
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Optimal brain damage
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Adam: A method for stochastic optimization
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Pruning filters for efficient convnets
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Xnor-net: Imagenet classification using binary convolutional neural networks
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Learning structured sparsity in deep neural networks
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Deep rewiring: Training very sparse deep networks
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Mask r-cnn
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The state of sparsity in deep neural networks
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Bag of tricks for image classification with convolutional neural networks
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Non-structured dnn weight pruning considered harmful
Yanzhi Wang, Shaokai Ye, Zhezhi He, Xiaolong Ma, Linfeng Zhang, Sheng Lin, Geng Yuan, Sia Huat Tan, Zhengang Li, Deliang Fan, et al · 2019
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Discovering neural wirings
Mitchell Wortsman, Ali Farhadi, and Mohammad Rastegari · 2019
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Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Attention is all you need
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Snip: Single-shot network pruning based on connection sensitivity
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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Understanding straight-through estimator in training activation quantized neural nets
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Language models are few-shot learners
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Pruning neural networks at initialization: Why are we missing the mark?
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Soft threshold weight reparameterization for learnable sparsity
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Topological insights into sparse neural networks
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Nvidia a100 tensor core gpu architecture
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