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Efficient data selection is essential for improving the training efficiency of deep neural networks and reducing the associated annotation costs.
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Faster cnns with direct sparse convolutions and guided pruning
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, and Pradeep Dubey · 2016
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Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar · 2017
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, and Ping Wang · 2019
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Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, et al · 2020
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Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Learning efficient convolutional networks through network slimming
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To prune, or not to prune: exploring the efficacy of pruning for model compression
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Active learning for convolutional neural networks: A core-set approach
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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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Lookahead: A far-sighted alternative of magnitude-based pruning
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Pruning neural networks without any data by iteratively conserving synaptic flow
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Pruning from scratch
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Single shot structured pruning before training
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Depgraph: Towards any structural pruning
Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, and Xinchao Wang · 2023
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Efficient data subset selection to generalize training across models: Transductive and inductive networks
Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh Iyer, and Abir De · 2024
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