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Pruning is one of the predominant approaches for compressing deep neural networks (DNNs).
Über den variabilitätsbereich der koeffizienten von potenzreihen, die gegebene werte nicht annehmen
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IW-H Tsang, JT-Y Kwok, and Jacek M Zurada · 2006
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Maximum margin coresets for active and noise tolerant learning
Sariel Har-Peled, Dan Roth, and Dav Zimak · 2007
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On khachiyan’s algorithm for the computation of minimum-volume enclosing ellipsoids
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Sampling algorithms and coresets for \ \backslash ell_p regression
Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, and Michael W Mahoney · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg · 2009
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Coresets and sketches for high dimensional subspace approximation problems
Dan Feldman, Morteza Monemizadeh, Christian Sohler, and David P Woodruff · 2010
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Universal ε \varepsilon -approximators for integrals
Michael Langberg and Leonard J Schulman · 2010
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Scalable training of mixture models via coresets
Dan Feldman, Matthew Faulkner, and Andreas Krause · 2011
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Subspace embeddings for the l1-norm with applications
Christian Sohler and David P Woodruff · 2011
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A coreset-based semi-supverised clustering using one-class support vector machines
Lei Gu · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A near-linear algorithm for projective clustering integer points
Kasturi Varadarajan and Xin Xiao · 2012
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Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Extremum problems with inequalities as subsidiary conditions
Fritz John · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Compressing neural networks with the hashing trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen · 2015
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Compressing convolutional neural networks
Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen · 2015
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Lp row sampling by lewis weights
Michael B Cohen and Richard Peng · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
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Training cnns with low-rank filters for efficient image classification
Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin · 2015
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, and Victor S. Lempitsky · 2015
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Diversity networks: Neural network compression using determinantal point processes
Zelda Mariet and Suvrit Sra · 2015
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Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al · 2015
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New frameworks for offline and streaming coreset constructions
Vladimir Braverman, Dan Feldman, and Harry Lang · 2016
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Coresets for scalable bayesian logistic regression
Jonathan Huggins, Trevor Campbell, and Tamara Broderick · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 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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Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Collaborative channel pruning for deep networks
Hanyu Peng, Jiaxiang Wu, Shifeng Chen, and Junzhou Huang · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Fair coresets and streaming algorithms for fair k-means
Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler · 2019
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Strong coresets for hard and soft bregman clustering with applications to exponential family mixtures
Mario Lucic, Olivier Bachem, and Andreas Krause · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Jeff M Phillips · 2016
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Net-trim: Convex pruning of deep neural networks with performance guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen, and Justin Romberg · 2017
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Compression-aware training of deep networks
Jose M Alvarez and Mathieu Salzmann · 2017
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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More is less: A more complicated network with less inference complexity
Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan · 2017
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Storage efficient and dynamic flexible runtime channel pruning via deep reinforcement learning
Jianda Chen, Shangyu Chen, and Sinno Jialin Pan · 2020
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Dan Feldman · 2020
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Campfire: Compressible, regularization-free, structured sparse training for hardware accelerators
Noah Gamboa, Kais Kudrolli, Anand Dhoot, and Ardavan Pedram · 2020
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Sets clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf, and Dan Feldman · 2020
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Operation-aware soft channel pruning using differentiable masks
Minsoo Kang and Bohyung Han · 2020
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Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Provable filter pruning for efficient neural networks
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, and Daniela Rus · 2020
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Dynamic model pruning with feedback
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, and Martin Jaggi · 2020
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Deep learning meets projective clustering
Alaa Maalouf, Harry Lang, Daniela Rus, and Dan Feldman · 2020
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Coresets for robust training of deep neural networks against noisy labels
Baharan Mirzasoleiman, Kaidi Cao, and Jure Leskovec · 2020
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Comparing fine-tuning and rewinding in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Woodfisher: Efficient second-order approximations for model compression
Sidak Pal Singh and Dan Alistarh · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
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Coresets for near-convex functions
Murad Tukan, Alaa Maalouf, and Dan Feldman · 2020
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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
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Pruning from scratch
Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, and Xiaolin Hu · 2020
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Good subnetworks provably exist: Pruning via greedy forward selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, and Qiang Liu · 2020
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Tighter m-DPP Coreset Sample Complexity Bounds
Gantavya Bhatt and Jeff Bilmes · 2021
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Solving linear programs in the current matrix multiplication time
Michael B Cohen, Yin Tat Lee, and Zhao Song · 2021
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Coresets for decision trees of signals
Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, and Dan Feldman · 2021
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Compressing neural networks: Towards determining the optimal layer-wise decomposition
Lucas Liebenwein, Alaa Maalouf, Dan Feldman, and Daniela Rus · 2021
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A unified approach to coreset learning
Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, and Margarita Osadchy · 2021
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Introduction to coresets: Approximated mean
Alaa Maalouf, Ibrahim Jubran, and Dan Feldman · 2021
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Coresets for the average case error for finite query sets
Alaa Maalouf, Ibrahim Jubran, Murad Tukan, and Dan Feldman · 2021
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Coresets for classification–simplified and strengthened
Tung Mai, Anup B Rao, and Cameron Musco · 2021
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Data-independent structured pruning of neural networks via coresets
Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, and Margarita Osadchy · 2021
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On coresets for support vector machines
Murad Tukan, Cenk Baykal, Dan Feldman, and Daniela Rus · 2021
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No fine-tuning, no cry: Robust svd for compressing deep networks
Murad Tukan, Alaa Maalouf, Matan Weksler, and Dan Feldman · 2021
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All resulted pruned models presented in this paper, 2022
Code · 2022
Closest in time.
Fast and accurate least-mean-squares solvers for high dimensional data
Alaa Maalouf, Ibrahim Jubran, and Danny Feldman · 2022
Closest in time.
Coresets for data discretization and sine wave fitting
Alaa Maalouf, Murad Tukan, Eric Price, Daniel M Kane, and Dan Feldman · 2022
Closest in time.
Obstacle aware sampling for path planning
Murad Tukan, Alaa Maalouf, Dan Feldman, and Roi Poranne · 2022
Closest in time.
New coresets for projective clustering and applications
Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, and Dan Feldman · 2022
Closest in time.