Fetching the paper…
Reading the bibliography…
Soft threshold pruning is among the cutting-edge pruning methods with state-of-the-art performance.
Régularisation d’inéquations variationnelles par approximations successives
Bernard Martinet · 1970
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
Earlier work this paper cites.
Johann faulhaber and sums of powers
Donald E Knuth · 1993
Earlier work this paper cites.
Sparse approximate solutions to linear systems
Balas Kausik Natarajan · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Adaptive greedy approximations
Geoff Davis, Stephane Mallat, and Marco Avellaneda · 1997
Earlier work this paper cites.
Networks of spiking neurons: The third generation of neural network models
Wolfgang Maass · 1997
Earlier work this paper cites.
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Ingrid Daubechies, Michel Defrise, and Christine De Mol · 2004
Earlier work this paper cites.
Signal recovery by proximal forward-backward splitting
Patrick L. Combettes and Valérie R. Wajs · 2005
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J. Candès, Justin K. Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Compressed sensing
David L Donoho · 2006
Earlier work this paper cites.
Why simple shrinkage is still relevant for redundant representations?
Michael Elad · 2006
Earlier work this paper cites.
A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration
JosÉ M. Bioucas-Dias and MÁrio A. T. Figueiredo · 2007
Earlier work this paper cites.
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
MÁrio A. T. Figueiredo, Robert D. Nowak, and Stephen J. Wright · 2007
Earlier work this paper cites.
Sparse MRI: The application of compressed sensing for rapid MR imaging
Michael Lustig, David Donoho, and John M. Pauly · 2007
Earlier work this paper cites.
The restricted isometry property and its implications for compressed sensing
Emmanuel J. Candès · 2008
Earlier work this paper cites.
On some series representations of the Hurwitz zeta function
Mark W Coffey · 2008
Earlier work this paper cites.
Fixed-point continuation for ℓ 1 \ell_{1} -minimization: Methodology and convergence
Elaine T. Hale, Wotao Yin, and Yin Zhang · 2008
Earlier work this paper cites.
Non-parametric seismic data recovery with curvelet frames
Felix J. Herrmann and Gilles Hennenfent · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Sparse reconstruction by separable approximation
Stephen J. Wright, Robert D. Nowak, and MÁrio A. T. Figueiredo · 2009
Earlier work this paper cites.
A subband adaptive iterative shrinkage/thresholding algorithm
Ilker Bayram and Ivan W. Selesnick · 2010
Earlier work this paper cites.
NESTA: A fast and accurate first-order method for sparse recovery
Stephen Becker, Jérôme Bobin, and Emmanuel J. Candès · 2011
Earlier work this paper cites.
A note on the complexity of L p L_{p} minimization
Dongdong Ge, Xiaoye Jiang, and Yinyu Ye · 2011
Earlier work this paper cites.
A proximal-gradient homotopy method for the l1-regularized least-squares problem
Lin Xiao and Tong Zhang · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Nonlocally centralized sparse representation for image restoration
Weisheng Dong, Lei Zhang, Guangming Shi, and Xin Li · 2013
Earlier work this paper cites.
A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems
Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Huang, and Jieping Ye · 2013
Earlier work this paper cites.
A proximal-gradient homotopy method for the sparse least-squares problem
Lin Xiao and Tong Zhang · 2013
Cited alongside, same era.
An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
Qihang Lin and Lin Xiao · 2014
Cited alongside, same era.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, Andrew S. Cassidy, Jun Sawada, Filipp Akopyan, Bryan L. Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, Bernard Brezzo, Ivan Vo, Steven K. Esser, Rathinakumar Appuswamy, Brian Taba, Arnon Amir, Myron D. Flickner, William P. Risk, Rajit Manohar, and Dharmendra S. Modha · 2014
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Cited alongside, same era.
Parsenet: Looking wider to see better
Wei Liu, Andrew Rabinovich, and Alexander Berg · 2015
Top-kast: Top-k always sparse training
Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, and Erich Elsen · 2020
Later among the works it cites.
Position-based scaled gradient for model quantization and pruning
Jangho Kim, KiYoon Yoo, and Nojun Kwak · 2020
Later among the works it cites.
Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
Later among the works it cites.
Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 2020
Later among the works it cites.
Woodfisher: Efficient second-order approximation for neural network compression
Sidak Pal Singh and Dan Alistarh · 2020
Later among the works it cites.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components
Ricardo Otazo, Emmanuel Candès, and Daniel K. Sodickson · 2015
Cited alongside, same era.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
First-Order Methods in Optimization , pp. 109
Amir Beck · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Later among the works it cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Later among the works it cites.
Drawing early-bird tickets: Toward more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, and Yingyan Lin · 2020
Later among the works it cites.
Neuron-level structured pruning using polarization regularizer
Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, and Xiang Li · 2020
Later among the works it cites.
Pruning of deep spiking neural networks through gradient rewiring
Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, and Yonghong Tian · 2021
Later among the works it cites.
Comprehensive snn compression using admm optimization and activity regularization
Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, and Yuan Xie · 2021
Later among the works it cites.
Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
Later among the works it cites.
Robust pruning at initialization
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, and Yee Whye Teh · 2021
Later among the works it cites.
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
Later among the works it cites.
Spike-thrift: Towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression
Souvik Kundu, Gourav Datta, Massoud Pedram, and Peter A. Beerel · 2021
Later among the works it cites.
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu, Peter E Latham, and Yee Teh · 2021
Later among the works it cites.
A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima
Zeke Xie, Issei Sato, and Masashi Sugiyama · 2021
Later among the works it cites.
Energy-efficient models for high-dimensional spike train classification using sparse spiking neural networks
Hang Yin, John Boaz Lee, Xiangnan Kong, Thomas Hartvigsen, and Sihong Xie · 2021
Later among the works it cites.
Effective sparsification of neural networks with global sparsity constraint
Xiao Zhou, Weizhong Zhang, Hang Xu, and Tong Zhang · 2021
Later among the works it cites.
Prospect pruning: Finding trainable weights at initialization using meta-gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, and Yarin Gal · 2022
Later among the works it cites.
State transition of dendritic spines improves learning of sparse spiking neural networks
Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, and Yonghong Tian · 2022
Later among the works it cites.
Neural architecture search for spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
Later among the works it cites.
Exploring lottery ticket hypothesis in spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, and Priyadarshini Panda · 2022
Later among the works it cites.
AutoSNN: Towards energy-efficient spiking neural networks
Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, and Sungroh Yoon · 2022
Later among the works it cites.
Winning the lottery ahead of time: Efficient early network pruning
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, and Stephan Günnemann · 2022
Later among the works it cites.
Learning soft threshold for sparse reparameterization using gradual projection operators
Xiaodong Wang, Xianxian Zeng, Yun Zhang, Dong Li, and Weijun Yang · 2022
Later among the works it cites.
Optimizing gradient-driven criteria in network sparsity: Gradient is all you need
Yuxin Zhang, Mingbao Lin, Mengzhao Chen, Zihan Xu, Fei Chao, Yunhan Shen, Ke Li, Yongjian Wu, and Rongrong Ji · 2022
Later among the works it cites.