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Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.
Scaling laws for ising models near T c {T}_{c}
Kadanoff, L. P · 1966
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Existence of a phase-transition in a one-dimensional ising ferromagnet
Dyson, F. J · 1969
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More is different
Anderson, P. W · 1972
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Critical exponents in 3.99 dimensions
Wilson, K. G. and Fisher, M. E · 1972
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The renormalization group: Critical phenomena and the kondo problem
Wilson, K. G · 1975
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Renormalization group by monte carlo methods
Ma, S.-k · 1976
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A real-space renormalization group for site and bond percolation
Reynolds, P. J., Stanley, H., and Klein, W · 1977
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Monte carlo renormalization group
Swendsen, R. H · 1979
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Monte carlo renormalization-group analysis of the classical heisenberg model in two dimensions
Shenker, S. H. and Tobochnik, J · 1980
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Quasiperiodicity in dissipative systems: a renormalization group analysis
Feigenbaum, M. J., Kadanoff, L. P., and Shenker, S. J · 1982
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Monte carlo renormalization-group study of ising spin glasses
Wang, J.-S. and Swendsen, R. H · 1988
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Optimal brain damage
LeCun, Y., Denker, J. S., and Solla, S. A · 1989
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Statistics of natural images: Scaling in the woods
Ruderman, D. L. and Bialek, W · 1994
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Universality classes of inelastic electron scattering cross-sections
Tougaard, S · 1997
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Rheology of Polymers Near Liquid-Solid Transitions , pp. 165–234
Winter, H. H. and Mours, M · 1997
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Renormalization group analysis of the small-world network model
Newman, M. E. and Watts, D. J · 1999
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Scaling exponents for barkhausen avalanches in polycrystalline and amorphous ferromagnets
Durin, G. and Zapperi, S · 2000
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SciPy: Open source scientific tools for Python, 2001–
Jones, E., Oliphant, T., Peterson, P., et al · 2001
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Lectures on Phase Transitions and the Renormalization Group
Goldenfeld, N · 2005
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Spectral properties of dynamical systems, model reduction and decompositions
Mezić, I · 2005
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Universality in three-dimensional ising spin glasses: A monte carlo study
Katzgraber, H. G., Körner, M., and Young, A · 2006
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Universally sloppy parameter sensitivities in systems biology models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., and Sethna, J. P · 2007
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Universality and universal finite-size scaling functions in four-dimensional ising spin glasses
Jörg, T. and Katzgraber, H. G · 2008
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Universal behavior for magnetic entropy change in magnetocaloric materials: An analysis on the nature of phase transitions
Bonilla, C. M., Herrero-Albillos, J., Bartolomé, F., García, L. M., Parra-Borderías, M., and Franco, V · 2010
Cited alongside, same era.
Parameter space compression underlies emergent theories and predictive models
Machta, B. B., Chachra, R., Transtrum, M. K., and Sethna, J. P · 2013
Cited alongside, same era.
Hierarchical model of natural images and the origin of scale invariance
Saremi, S. and Sejnowski, T. J · 2013
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Many-body localization in one dimension as a dynamical renormalization group fixed point
Vosk, R. and Altman, E · 2013
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An exact mapping between the variational renormalization group and deep learning
Mehta, P. and Schwab, D. J · 2014
Cited alongside, same era.
[Re] One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Gohil, V., Narayanan, S. D., and Jain, A · 2020
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Is deep learning a renormalization group flow?
Koch, E. D. M., Koch, R. D. M., and Cheng, L · 2020
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What is being transferred in transfer learning?
Neyshabur, B., Sedghi, H., and Zhang, C · 2020
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When BERT Plays the Lottery, All Tickets Are Winning
Prasanna, S., Rogers, A., and Rumshisky, A · 2020
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Renormalization group as a koopman operator
Redman, W. T · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Renda, A., Frankle, J., and Carbin, M · 2020
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Huh, M., Agrawal, P., and Efros, A. A · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Cited alongside, same era.
Comment on “why does deep and cheap learning work so well?” [arxiv:1608.08225], 2016
Schwab, D. J. and Mehta, P · 2016
Cited alongside, same era.
Pca meets rg
Bradde, S. and Bialek, W · 2017
Cited alongside, same era.
Why does deep and cheap learning work so well?
Lin, H. W., Tegmark, M., and Rolnick, D · 2017
Cited alongside, same era.
Gradient descent happens in a tiny subspace
Gur-Ari, G., Roberts, D. A., and Dyer, E · 2018
Cited alongside, same era.
Scale-invariant feature extraction of neural network and renormalization group flow
Iso, S., Shiba, S., and Yokoo, S · 2018
Cited alongside, same era.
On the predictability of pruning across scales
Rosenfeld, J. S., Frankle, J., Carbin, M., and Shavit, N · 2020
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Sanity-checking pruning methods: Random tickets can win the jackpot
Su, J., Chen, Y., Cai, T., Wu, T., Gao, R., Wang, L., and Lee, J. D · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L., and Ganguli, S · 2020
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Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
Yu, H., Edunov, S., Tian, Y., and Morcos, A. S · 2020
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Lottery tickets in linear models: An analysis of iterative magnitude pruning
Elesedy, B., Kanade, V., and Teh, Y. W · 2021
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Pruning neural networks at initialization: Why are we missing the mark?
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2021
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Vision transformers with patch diversification
Gong, C., Wang, D., Li, M., Chandra, V., and Liu, Q · 2021
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Relevance in the renormalization group and in information theory
Gordon, A., Banerjee, A., Koch-Janusz, M., and Ringel, Z · 2021
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Towards understanding iterative magnitude pruning: Why lottery tickets win
Maene, J., Li, M., and Moens, M.-F · 2021
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The principles of deep learning theory
Roberts, D. A., Yaida, S., and Hanin, B · 2021
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On the transferability of winning tickets in non-natural image datasets
Sabatelli, M., Kestemont, M., and Geurts, P · 2021
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Going deeper with image transformers
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H · 2021
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Validating the lottery ticket hypothesis with inertial manifold theory
Zhang, Z., Jin, J., Zhang, Z., Zhou, Y., Zhao, X., Ren, J., Liu, J., Wu, L., Jin, R., and Dou, D · 2021
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Deepvit: Towards deeper vision transformer
Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., and Feng, J · 2021
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On the existence of universal lottery tickets
Burkholz, R., Laha, N., Mukherjee, R., and Gotovos, A · 2022
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Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces
Li, T., Tan, L., Huang, Z., Tao, Q., Liu, Y., and Huang, X · 2022
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