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2411.08798
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Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
13 November 2024
Berfin Simsek
Amire Bendjeddou
Daniel Hsu
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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
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Yujin Song
Taiji Suzuki
Denny Wu
MLT
62
9
0
17 Jun 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee
Kazusato Oko
Taiji Suzuki
Denny Wu
MLT
141
25
0
03 Jun 2024
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
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
Simon Martin
Francis Bach
Giulio Biroli
94
11
0
07 Nov 2023
Should Under-parameterized Student Networks Copy or Average Teacher Weights?
Berfin Simsek
Amire Bendjeddou
W. Gerstner
Johanni Brea
75
8
0
03 Nov 2023
On Learning Gaussian Multi-index Models with Gradient Flow
A. Bietti
Joan Bruna
Loucas Pillaud-Vivien
58
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
Margalit Glasgow
MLT
137
14
0
26 Sep 2023
On Single Index Models beyond Gaussian Data
Joan Bruna
Loucas Pillaud-Vivien
Aaron Zweig
78
11
0
28 Jul 2023
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
94
39
0
18 May 2023
Expand-and-Cluster: Parameter Recovery of Neural Networks
Flavio Martinelli
Berfin Simsek
W. Gerstner
Johanni Brea
138
8
0
25 Apr 2023
Learning time-scales in two-layers neural networks
Raphael Berthier
Andrea Montanari
Kangjie Zhou
174
38
0
28 Feb 2023
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics
Emmanuel Abbe
Enric Boix-Adserà
Theodor Misiakiewicz
FedML
MLT
157
86
0
21 Feb 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron
Weihang Xu
S. Du
83
16
0
20 Feb 2023
Learning Single-Index Models with Shallow Neural Networks
A. Bietti
Joan Bruna
Clayton Sanford
M. Song
218
71
0
27 Oct 2022
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
Alexandru Damian
Jason D. Lee
Mahdi Soltanolkotabi
SSL
MLT
98
123
0
30 Jun 2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Etienne Boursier
Loucas Pillaud-Vivien
Nicolas Flammarion
ODL
68
61
0
02 Jun 2022
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
Yossi Arjevani
M. Field
63
19
0
21 Jul 2021
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
Sitan Chen
Raghu Meka
70
40
0
28 Apr 2020
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Gerard Ben Arous
Reza Gheissari
Aukosh Jagannath
80
91
0
23 Mar 2020
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
132
145
0
18 Jun 2019
Gradient Descent Quantizes ReLU Network Features
Hartmut Maennel
Olivier Bousquet
Sylvain Gelly
MLT
66
82
0
22 Mar 2018
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition
Marco Mondelli
Andrea Montanari
MLT
CML
75
59
0
20 Feb 2018
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
Amit Daniely
Roy Frostig
Y. Singer
170
345
0
18 Feb 2016
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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