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Modelling the influence of data structure on learning in neural networks: the hidden manifold model
25 September 2019
Sebastian Goldt
M. Mézard
Florent Krzakala
Lenka Zdeborová
BDL
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Papers citing
"Modelling the influence of data structure on learning in neural networks: the hidden manifold model"
12 / 12 papers shown
Title
Effects of Data Geometry in Early Deep Learning
Saket Tiwari
G. Konidaris
59
7
0
29 Dec 2022
Quantifying Relevance in Learning and Inference
M. Marsili
Y. Roudi
6
18
0
01 Feb 2022
The emergence of a concept in shallow neural networks
E. Agliari
Francesco Alemanno
Adriano Barra
G. D. Marzo
16
39
0
01 Sep 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model
Bruno Loureiro
Cédric Gerbelot
Hugo Cui
Sebastian Goldt
Florent Krzakala
M. Mézard
Lenka Zdeborová
15
135
0
16 Feb 2021
Deep Networks and the Multiple Manifold Problem
Sam Buchanan
D. Gilboa
John N. Wright
166
39
0
25 Aug 2020
Geometric compression of invariant manifolds in neural nets
J. Paccolat
Leonardo Petrini
Mario Geiger
Kevin Tyloo
M. Wyart
MLT
47
34
0
22 Jul 2020
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
Mehdi Abbana Bennani
Thang Doan
Masashi Sugiyama
CLL
42
61
0
21 Jun 2020
Fractional Deep Neural Network via Constrained Optimization
Harbir Antil
R. Khatri
R. Löhner
Deepanshu Verma
18
29
0
01 Apr 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime
Stéphane dÁscoli
Maria Refinetti
Giulio Biroli
Florent Krzakala
83
152
0
02 Mar 2020
Generalisation error in learning with random features and the hidden manifold model
Federica Gerace
Bruno Loureiro
Florent Krzakala
M. Mézard
Lenka Zdeborová
22
165
0
21 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Ginevra Carbone
Matthew Wicker
Luca Laurenti
A. Patané
Luca Bortolussi
G. Sanguinetti
AAML
16
77
0
11 Feb 2020
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
Elisa Oostwal
Michiel Straat
Michael Biehl
MLT
56
54
0
16 Oct 2019
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