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An initial alignment between neural network and target is needed for
  gradient descent to learn

An initial alignment between neural network and target is needed for gradient descent to learn

25 February 2022
Emmanuel Abbe
Elisabetta Cornacchia
Jan Hązła
Christopher Marquis
ArXivPDFHTML

Papers citing "An initial alignment between neural network and target is needed for gradient descent to learn"

11 / 11 papers shown
Title
When can transformers reason with abstract symbols?
When can transformers reason with abstract symbols?
Enric Boix-Adserà
Omid Saremi
Emmanuel Abbe
Samy Bengio
Etai Littwin
Josh Susskind
LRM
NAI
31
12
0
15 Oct 2023
Provable Advantage of Curriculum Learning on Parity Targets with Mixed
  Inputs
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
Emmanuel Abbe
Elisabetta Cornacchia
Aryo Lotfi
28
11
0
29 Jun 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
26
4
0
25 Apr 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
FedML
MLT
79
72
0
21 Feb 2023
A Mathematical Model for Curriculum Learning for Parities
A Mathematical Model for Curriculum Learning for Parities
Elisabetta Cornacchia
Elchanan Mossel
34
10
0
31 Jan 2023
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Emmanuel Abbe
Samy Bengio
Aryo Lotfi
Kevin Rizk
LRM
36
48
0
30 Jan 2023
On the non-universality of deep learning: quantifying the cost of
  symmetry
On the non-universality of deep learning: quantifying the cost of symmetry
Emmanuel Abbe
Enric Boix-Adserà
FedML
MLT
30
18
0
05 Aug 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
27
123
0
18 Jul 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
19
58
0
02 Jun 2022
Learning to Reason with Neural Networks: Generalization, Unseen Data and
  Boolean Measures
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Emmanuel Abbe
Samy Bengio
Elisabetta Cornacchia
Jon M. Kleinberg
Aryo Lotfi
M. Raghu
Chiyuan Zhang
MLT
13
10
0
26 May 2022
On the Power of Differentiable Learning versus PAC and SQ Learning
On the Power of Differentiable Learning versus PAC and SQ Learning
Emmanuel Abbe
Pritish Kamath
Eran Malach
Colin Sandon
Nathan Srebro
MLT
74
23
0
09 Aug 2021
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