Fetching the paper…
Reading the bibliography…
A striking observation about iterative magnitude pruning (IMP; Frankle et al.
Pruning versus clipping in neural networks
S. A. Janowsky · 1989
Earlier work this paper cites.
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
The cifar-10 dataset
A. Krizhevsky, V. Nair, and G. Hinton · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Earlier work this paper cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
M. Zhu and S. Gupta · 2017
Earlier work this paper cites.
Cinic-10 is not imagenet or cifar-10
L. N. Darlow, E. J. Crowley, A. Antoniou, and A. J. Storkey · 2018
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2018
Cited alongside, same era.
Loss surfaces, mode connectivity, and fast ensembling of dnns
T. Garipov, P. Izmailov, D. Podoprikhin, D. P. Vetrov, and A. G. Wilson · 2018
Cited alongside, same era.
Explaining landscape connectivity of low-cost solutions for multilayer nets
R. Kuditipudi, X. Wang, H. Lee, Y. Zhang, Z. Li, W. Hu, R. Ge, and S. Arora · 2019
Cited alongside, same era.
Uniform convergence may be unable to explain generalization in deep learning
V. Nagarajan and J. Z. Kolter · 2019
Cited alongside, same era.
The lottery ticket hypothesis for pre-trained bert networks
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin · 2020
Cited alongside, same era.
Rigging the lottery: Making all tickets winners
Winning lottery tickets in deep generative models
N. M. Kalibhat, Y. Balaji, and S. Feizi · 2020
Later among the works it cites.
Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
H. Yu, S. Edunov, Y. Tian, and A. S. Morcos · 2020
Later among the works it cites.
Deep learning through the lens of example difficulty
R. Baldock, H. Maennel, and B. Neyshabur · 2021
Later among the works it cites.
The role of permutation invariance in linear mode connectivity of neural networks
R. Entezari, H. Sedghi, O. Saukh, and B. Neyshabur · 2021
Later among the works it cites.
A loss curvature perspective on training instabilities of deep learning models
J. Gilmer, B. Ghorbani, A. Garg, S. Kudugunta, B. Neyshabur, D. Cardoze, G. E. Dahl, Z. Nado, and O. Firat · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
U. Evci, T. Gale, J. Menick, P. S. Castro, and E. Elsen · 2020
Cited alongside, same era.
S. Fort, G. K. Dziugaite, M. Paul, S. Kharaghani, D. M. Roy, and S. Ganguli · 2020
Cited alongside, same era.
Essentially no barriers in neural network energy landscape
F. Draxler, K. Veschgini, M. Salmhofer, and F. Hamprecht
Cited in the paper.
Essentially no barriers in neural network energy landscape
F. Draxler, K. Veschgini, M. Salmhofer, and F. Hamprecht
Cited in the paper.
Linear mode connectivity and the lottery ticket hypothesis
J. Frankle, G. K. Dziugaite, D. M. Roy, and M. Carbin
Cited in the paper.
The early phase of neural network training
J. Frankle, D. J. Schwab, and A. S. Morcos
Cited in the paper.
Deep learning on a data diet: Finding important examples early in training
M. Paul, S. Ganguli, and G. K. Dziugaite · 2021
Later among the works it cites.
On lottery tickets and minimal task representations in deep reinforcement learning
M. A. Vischer, R. T. Lange, and H. Sprekeler · 2021
Later among the works it cites.