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On Lazy Training in Differentiable Programming

On Lazy Training in Differentiable Programming

19 December 2018
Lénaïc Chizat
Edouard Oyallon
Francis R. Bach
ArXivPDFHTML

Papers citing "On Lazy Training in Differentiable Programming"

50 / 246 papers shown
Title
Deformed semicircle law and concentration of nonlinear random matrices
  for ultra-wide neural networks
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
Zhichao Wang
Yizhe Zhu
37
18
0
20 Sep 2021
Uniform Generalization Bounds for Overparameterized Neural Networks
Uniform Generalization Bounds for Overparameterized Neural Networks
Sattar Vakili
Michael Bromberg
Jezabel R. Garcia
Da-shan Shiu
A. Bernacchia
25
19
0
13 Sep 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of
  Overparameterized Machine Learning
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar
Vidya Muthukumar
Richard G. Baraniuk
36
71
0
06 Sep 2021
Dash: Semi-Supervised Learning with Dynamic Thresholding
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Tian Xu
Lei Shang
Jinxing Ye
Qi Qian
Yu-Feng Li
Baigui Sun
Hao Li
Rong Jin
47
218
0
01 Sep 2021
Understanding the Generalization of Adam in Learning Neural Networks
  with Proper Regularization
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Difan Zou
Yuan Cao
Yuanzhi Li
Quanquan Gu
MLT
AI4CE
47
39
0
25 Aug 2021
Convergence analysis for gradient flows in the training of artificial
  neural networks with ReLU activation
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation
Arnulf Jentzen
Adrian Riekert
27
23
0
09 Jul 2021
Continual Learning in the Teacher-Student Setup: Impact of Task
  Similarity
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
Sebastian Lee
Sebastian Goldt
Andrew M. Saxe
CLL
32
73
0
09 Jul 2021
Small random initialization is akin to spectral learning: Optimization
  and generalization guarantees for overparameterized low-rank matrix
  reconstruction
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
Dominik Stöger
Mahdi Soltanolkotabi
ODL
42
75
0
28 Jun 2021
Locality defeats the curse of dimensionality in convolutional
  teacher-student scenarios
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios
Alessandro Favero
Francesco Cagnetta
M. Wyart
30
31
0
16 Jun 2021
A self consistent theory of Gaussian Processes captures feature learning
  effects in finite CNNs
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Gadi Naveh
Zohar Ringel
SSL
MLT
36
31
0
08 Jun 2021
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width
  Limit at Initialization
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization
Mufan Li
Mihai Nica
Daniel M. Roy
35
33
0
07 Jun 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCV
BDL
33
124
0
14 May 2021
Global Convergence of Three-layer Neural Networks in the Mean Field
  Regime
Global Convergence of Three-layer Neural Networks in the Mean Field Regime
H. Pham
Phan-Minh Nguyen
MLT
AI4CE
41
19
0
11 May 2021
Relative stability toward diffeomorphisms indicates performance in deep
  nets
Relative stability toward diffeomorphisms indicates performance in deep nets
Leonardo Petrini
Alessandro Favero
Mario Geiger
M. Wyart
OOD
38
15
0
06 May 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features
A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu
Tianyu Ding
Jinxin Zhou
Xiao Li
Chong You
Jeremias Sulam
Qing Qu
40
196
0
06 May 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg
Sivaraman Balakrishnan
J. Zico Kolter
Zachary Chase Lipton
32
30
0
01 May 2021
Generalization Guarantees for Neural Architecture Search with
  Train-Validation Split
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Samet Oymak
Mingchen Li
Mahdi Soltanolkotabi
AI4CE
OOD
36
13
0
29 Apr 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss
  Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
172
28
0
22 Apr 2021
Understanding Overparameterization in Generative Adversarial Networks
Understanding Overparameterization in Generative Adversarial Networks
Yogesh Balaji
M. Sajedi
Neha Kalibhat
Mucong Ding
Dominik Stöger
Mahdi Soltanolkotabi
S. Feizi
AI4CE
22
21
0
12 Apr 2021
Landscape analysis for shallow neural networks: complete classification
  of critical points for affine target functions
Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions
Patrick Cheridito
Arnulf Jentzen
Florian Rossmannek
24
10
0
19 Mar 2021
Computing the Information Content of Trained Neural Networks
Computing the Information Content of Trained Neural Networks
Jeremy Bernstein
Yisong Yue
27
4
0
01 Mar 2021
Experiments with Rich Regime Training for Deep Learning
Experiments with Rich Regime Training for Deep Learning
Xinyan Li
A. Banerjee
32
2
0
26 Feb 2021
Do Input Gradients Highlight Discriminative Features?
Do Input Gradients Highlight Discriminative Features?
Harshay Shah
Prateek Jain
Praneeth Netrapalli
AAML
FAtt
26
57
0
25 Feb 2021
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal
  Mirror Descent
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay
E. Moroshko
Mor Shpigel Nacson
Blake E. Woodworth
Nathan Srebro
Amir Globerson
Daniel Soudry
AI4CE
35
73
0
19 Feb 2021
Convergence of stochastic gradient descent schemes for
  Lojasiewicz-landscapes
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes
Steffen Dereich
Sebastian Kassing
34
27
0
16 Feb 2021
Explaining Neural Scaling Laws
Explaining Neural Scaling Laws
Yasaman Bahri
Ethan Dyer
Jared Kaplan
Jaehoon Lee
Utkarsh Sharma
27
250
0
12 Feb 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer
  Neural Network
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
Mo Zhou
Rong Ge
Chi Jin
76
45
0
04 Feb 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse
  in Imbalanced Training
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
Cong Fang
Hangfeng He
Qi Long
Weijie J. Su
FAtt
130
168
0
29 Jan 2021
A Priori Generalization Analysis of the Deep Ritz Method for Solving
  High Dimensional Elliptic Equations
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
Jianfeng Lu
Yulong Lu
Min Wang
36
37
0
05 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in
  the Presence of Adversarial Label Noise
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei
Yuan Cao
Quanquan Gu
FedML
MLT
70
19
0
04 Jan 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for
  Deep ReLU Networks
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh N. Nguyen
Marco Mondelli
Guido Montúfar
25
81
0
21 Dec 2020
Provable Benefits of Overparameterization in Model Compression: From
  Double Descent to Pruning Neural Networks
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Xiangyu Chang
Yingcong Li
Samet Oymak
Christos Thrampoulidis
35
50
0
16 Dec 2020
A semigroup method for high dimensional committor functions based on
  neural network
A semigroup method for high dimensional committor functions based on neural network
Haoya Li
Y. Khoo
Yinuo Ren
Lexing Ying
16
6
0
12 Dec 2020
On 1/n neural representation and robustness
On 1/n neural representation and robustness
Josue Nassar
Piotr A. Sokól
SueYeon Chung
K. Harris
Il Memming Park
AAML
OOD
24
23
0
08 Dec 2020
Gradient Starvation: A Learning Proclivity in Neural Networks
Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki
Sekouba Kaba
Yoshua Bengio
Aaron Courville
Doina Precup
Guillaume Lajoie
MLT
50
258
0
18 Nov 2020
On Function Approximation in Reinforcement Learning: Optimism in the
  Face of Large State Spaces
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang
Chi Jin
Zhaoran Wang
Mengdi Wang
Michael I. Jordan
39
18
0
09 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine
  Learning
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander DÁmour
Katherine A. Heller
D. Moldovan
Ben Adlam
B. Alipanahi
...
Kellie Webster
Steve Yadlowsky
T. Yun
Xiaohua Zhai
D. Sculley
OffRL
77
671
0
06 Nov 2020
A Dynamical View on Optimization Algorithms of Overparameterized Neural
  Networks
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
Zhiqi Bu
Shiyun Xu
Kan Chen
33
17
0
25 Oct 2020
Stable ResNet
Stable ResNet
Soufiane Hayou
Eugenio Clerico
Bo He
George Deligiannidis
Arnaud Doucet
Judith Rousseau
ODL
SSeg
46
51
0
24 Oct 2020
Global optimality of softmax policy gradient with single hidden layer
  neural networks in the mean-field regime
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime
Andrea Agazzi
Jianfeng Lu
13
15
0
22 Oct 2020
A Unifying View on Implicit Bias in Training Linear Neural Networks
A Unifying View on Implicit Bias in Training Linear Neural Networks
Chulhee Yun
Shankar Krishnan
H. Mobahi
MLT
18
80
0
06 Oct 2020
On the linearity of large non-linear models: when and why the tangent
  kernel is constant
On the linearity of large non-linear models: when and why the tangent kernel is constant
Chaoyue Liu
Libin Zhu
M. Belkin
21
140
0
02 Oct 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes
Deep Equals Shallow for ReLU Networks in Kernel Regimes
A. Bietti
Francis R. Bach
30
86
0
30 Sep 2020
How Neural Networks Extrapolate: From Feedforward to Graph Neural
  Networks
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu
Mozhi Zhang
Jingling Li
S. Du
Ken-ichi Kawarabayashi
Stefanie Jegelka
MLT
25
306
0
24 Sep 2020
Generalized Leverage Score Sampling for Neural Networks
Generalized Leverage Score Sampling for Neural Networks
J. Lee
Ruoqi Shen
Zhao Song
Mengdi Wang
Zheng Yu
21
42
0
21 Sep 2020
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
Zuyue Fu
Zhuoran Yang
Zhaoran Wang
21
42
0
02 Aug 2020
The Interpolation Phase Transition in Neural Networks: Memorization and
  Generalization under Lazy Training
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training
Andrea Montanari
Yiqiao Zhong
49
95
0
25 Jul 2020
Geometric compression of invariant manifolds in neural nets
Geometric compression of invariant manifolds in neural nets
J. Paccolat
Leonardo Petrini
Mario Geiger
Kevin Tyloo
M. Wyart
MLT
55
34
0
22 Jul 2020
Phase diagram for two-layer ReLU neural networks at infinite-width limit
Phase diagram for two-layer ReLU neural networks at infinite-width limit
Tao Luo
Zhi-Qin John Xu
Zheng Ma
Yaoyu Zhang
22
59
0
15 Jul 2020
Explicit Regularisation in Gaussian Noise Injections
Explicit Regularisation in Gaussian Noise Injections
A. Camuto
M. Willetts
Umut Simsekli
Stephen J. Roberts
Chris Holmes
25
55
0
14 Jul 2020
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