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Maximum Multiscale Entropy and Neural Network Regularization

Maximum Multiscale Entropy and Neural Network Regularization

25 June 2020
Amir-Reza Asadi
Emmanuel Abbe
ArXivPDFHTML

Papers citing "Maximum Multiscale Entropy and Neural Network Regularization"

18 / 18 papers shown
Title
Chaining Meets Chain Rule: Multilevel Entropic Regularization and
  Training of Neural Nets
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets
Amir-Reza Asadi
Emmanuel Abbe
BDL
AI4CE
54
13
0
26 Jun 2019
Is Deep Learning a Renormalization Group Flow?
Is Deep Learning a Renormalization Group Flow?
E. Koch
R. Koch
Ling Cheng
OOD
AI4CE
16
6
0
12 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes
Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz Khan
Alexander Immer
Ehsan Abedi
M. Korzepa
UQCV
BDL
58
124
0
05 Jun 2019
Thermodynamics and Feature Extraction by Machine Learning
Thermodynamics and Feature Extraction by Machine Learning
S. Funai
D. Giataganas
DRL
AI4CE
28
34
0
18 Oct 2018
Chaining Mutual Information and Tightening Generalization Bounds
Chaining Mutual Information and Tightening Generalization Bounds
Amir-Reza Asadi
Emmanuel Abbe
S. Verdú
AI4CE
15
121
0
11 Jun 2018
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian
  Compression Approach
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Wenda Zhou
Victor Veitch
Morgane Austern
Ryan P. Adams
Peter Orbanz
53
212
0
16 Apr 2018
Scale-invariant Feature Extraction of Neural Network and Renormalization
  Group Flow
Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow
S. Iso
Shotaro Shiba
Sumito Yokoo
OOD
AI4CE
45
70
0
22 Jan 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization
  properties of Entropy-SGD and data-dependent priors
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Gintare Karolina Dziugaite
Daniel M. Roy
MLT
45
145
0
26 Dec 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for
  Neural Networks
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur
Srinadh Bhojanapalli
Nathan Srebro
49
604
0
29 Jul 2017
Information-theoretic analysis of generalization capability of learning
  algorithms
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu
Maxim Raginsky
74
442
0
22 May 2017
Mutual Information, Neural Networks and the Renormalization Group
Mutual Information, Neural Networks and the Renormalization Group
M. Koch-Janusz
Zohar Ringel
DRL
AI4CE
53
176
0
20 Apr 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural
  Networks with Many More Parameters than Training Data
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Gintare Karolina Dziugaite
Daniel M. Roy
67
808
0
31 Mar 2017
Identity Matters in Deep Learning
Identity Matters in Deep Learning
Moritz Hardt
Tengyu Ma
OOD
45
398
0
14 Nov 2016
Why does deep and cheap learning work so well?
Why does deep and cheap learning work so well?
Henry W. Lin
Max Tegmark
David Rolnick
53
606
0
29 Aug 2016
How much does your data exploration overfit? Controlling bias via
  information usage
How much does your data exploration overfit? Controlling bias via information usage
D. Russo
James Zou
26
189
0
16 Nov 2015
An exact mapping between the Variational Renormalization Group and Deep
  Learning
An exact mapping between the Variational Renormalization Group and Deep Learning
Pankaj Mehta
D. Schwab
AI4CE
31
309
0
14 Oct 2014
Rényi Divergence and Kullback-Leibler Divergence
Rényi Divergence and Kullback-Leibler Divergence
T. Erven
P. Harremoes
39
1,326
0
12 Jun 2012
Pac-Bayesian Supervised Classification: The Thermodynamics of
  Statistical Learning
Pac-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning
O. Catoni
213
458
0
03 Dec 2007
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