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Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
v1v2 (latest)

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence

13 November 2024
Berfin Simsek
Amire Bendjeddou
Daniel Hsu
ArXiv (abs)PDFHTML

Papers citing "Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence"

29 / 29 papers shown
Title
Learning sum of diverse features: computational hardness and efficient
  gradient-based training for ridge combinations
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
Kazusato Oko
Yujin Song
Taiji Suzuki
Denny Wu
MLT
46
9
0
17 Jun 2024
Neural network learns low-dimensional polynomials with SGD near the
  information-theoretic limit
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee
Kazusato Oko
Taiji Suzuki
Denny Wu
MLT
139
25
0
03 Jun 2024
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
Luca Arnaboldi
Yatin Dandi
Florent Krzakala
Luca Pesce
Ludovic Stephan
119
18
0
24 May 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer
  Networks: Breaking the Curse of Information and Leap Exponents
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
Yatin Dandi
Emanuele Troiani
Luca Arnaboldi
Luca Pesce
Lenka Zdeborová
Florent Krzakala
MLT
113
30
0
05 Feb 2024
On the Impact of Overparameterization on the Training of a Shallow
  Neural Network in High Dimensions
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions
Simon Martin
Francis Bach
Giulio Biroli
86
11
0
07 Nov 2023
Should Under-parameterized Student Networks Copy or Average Teacher
  Weights?
Should Under-parameterized Student Networks Copy or Average Teacher Weights?
Berfin Simsek
Amire Bendjeddou
W. Gerstner
Johanni Brea
71
8
0
03 Nov 2023
On Learning Gaussian Multi-index Models with Gradient Flow
On Learning Gaussian Multi-index Models with Gradient Flow
A. Bietti
Joan Bruna
Loucas Pillaud-Vivien
48
37
0
30 Oct 2023
SGD Finds then Tunes Features in Two-Layer Neural Networks with
  near-Optimal Sample Complexity: A Case Study in the XOR problem
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem
Margalit Glasgow
MLT
129
14
0
26 Sep 2023
On Single Index Models beyond Gaussian Data
On Single Index Models beyond Gaussian Data
Joan Bruna
Loucas Pillaud-Vivien
Aaron Zweig
76
11
0
28 Jul 2023
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample
  Complexity for Learning Single Index Models
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
Alexandru Damian
Eshaan Nichani
Rong Ge
Jason D. Lee
MLT
91
39
0
18 May 2023
Expand-and-Cluster: Parameter Recovery of Neural Networks
Expand-and-Cluster: Parameter Recovery of Neural Networks
Flavio Martinelli
Berfin Simsek
W. Gerstner
Johanni Brea
124
8
0
25 Apr 2023
Learning time-scales in two-layers neural networks
Learning time-scales in two-layers neural networks
Raphael Berthier
Andrea Montanari
Kangjie Zhou
159
38
0
28 Feb 2023
SGD learning on neural networks: leap complexity and saddle-to-saddle
  dynamics
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics
Emmanuel Abbe
Enric Boix-Adserà
Theodor Misiakiewicz
FedMLMLT
157
86
0
21 Feb 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for
  Learning a Single Neuron
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron
Weihang Xu
S. Du
78
16
0
20 Feb 2023
Learning Single-Index Models with Shallow Neural Networks
Learning Single-Index Models with Shallow Neural Networks
A. Bietti
Joan Bruna
Clayton Sanford
M. Song
213
71
0
27 Oct 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the
  Computational Limit
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak
Benjamin L. Edelman
Surbhi Goel
Sham Kakade
Eran Malach
Cyril Zhang
108
133
0
18 Jul 2022
Neural Networks can Learn Representations with Gradient Descent
Neural Networks can Learn Representations with Gradient Descent
Alexandru Damian
Jason D. Lee
Mahdi Soltanolkotabi
SSLMLT
98
123
0
30 Jun 2022
Gradient flow dynamics of shallow ReLU networks for square loss and
  orthogonal inputs
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Etienne Boursier
Loucas Pillaud-Vivien
Nicolas Flammarion
ODL
66
61
0
02 Jun 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step
  Improves the Representation
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Jimmy Ba
Murat A. Erdogdu
Taiji Suzuki
Zhichao Wang
Denny Wu
Greg Yang
MLT
91
129
0
03 May 2022
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural
  Networks: A Tale of Symmetry II
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II
Yossi Arjevani
M. Field
58
19
0
21 Jul 2021
Geometry of the Loss Landscape in Overparameterized Neural Networks:
  Symmetries and Invariances
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
Berfin cSimcsek
François Ged
Arthur Jacot
Francesco Spadaro
Clément Hongler
W. Gerstner
Johanni Brea
AI4CE
82
102
0
25 May 2021
Learning Polynomials of Few Relevant Dimensions
Learning Polynomials of Few Relevant Dimensions
Sitan Chen
Raghu Meka
62
40
0
28 Apr 2020
Online stochastic gradient descent on non-convex losses from
  high-dimensional inference
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Gerard Ben Arous
Reza Gheissari
Aukosh Jagannath
77
91
0
23 Mar 2020
Dynamics of stochastic gradient descent for two-layer neural networks in
  the teacher-student setup
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Sebastian Goldt
Madhu S. Advani
Andrew M. Saxe
Florent Krzakala
Lenka Zdeborová
MLT
121
145
0
18 Jun 2019
Gradient Descent Quantizes ReLU Network Features
Gradient Descent Quantizes ReLU Network Features
Hartmut Maennel
Olivier Bousquet
Sylvain Gelly
MLT
61
82
0
22 Mar 2018
On the Connection Between Learning Two-Layers Neural Networks and Tensor
  Decomposition
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition
Marco Mondelli
Andrea Montanari
MLTCML
75
59
0
20 Feb 2018
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran
Ohad Shamir
184
265
0
24 Dec 2017
Toward Deeper Understanding of Neural Networks: The Power of
  Initialization and a Dual View on Expressivity
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely
Roy Frostig
Y. Singer
170
345
0
18 Feb 2016
Tensor decompositions for learning latent variable models
Tensor decompositions for learning latent variable models
Anima Anandkumar
Rong Ge
Daniel J. Hsu
Sham Kakade
Matus Telgarsky
464
1,150
0
29 Oct 2012
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