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Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian
  Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
v1v2v3 (latest)

Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

13 February 2019
Greg Yang
ArXiv (abs)PDFHTML

Papers citing "Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation"

50 / 211 papers shown
Title
Viability of perturbative expansion for quantum field theories on neurons
Viability of perturbative expansion for quantum field theories on neurons
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Asymptotic convexity of wide and shallow neural networks
Asymptotic convexity of wide and shallow neural networks
Vivek Borkar
Parthe Pandit
186
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0
23 Jun 2025
Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size
Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size
Soufiane Hayou
Liyuan Liu
105
2
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17 Jun 2025
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
Shiwei Li
Xiandi Luo
Xing Tang
Haozhao Wang
Hao Chen
Weihong Luo
Yuhua Li
Xiuqiang He
Ruixuan Li
AI4CE
165
7
0
29 May 2025
Universal Value-Function Uncertainties
Universal Value-Function Uncertainties
Moritz A. Zanger
Max Weltevrede
Yaniv Oren
Pascal R. van der Vaart
Caroline Horsch
Wendelin Bohmer
M. Spaan
OffRL
262
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0
27 May 2025
A ZeNN architecture to avoid the Gaussian trap
A ZeNN architecture to avoid the Gaussian trap
Luís Carvalho
Joao L. Costa
José Mourao
Gonçalo Oliveira
214
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0
26 May 2025
New Evidence of the Two-Phase Learning Dynamics of Neural Networks
New Evidence of the Two-Phase Learning Dynamics of Neural Networks
Zhanpeng Zhou
Yongyi Yang
Mahito Sugiyama
Junchi Yan
179
2
0
20 May 2025
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
Xin Yu
Yujia Wang
Jinghui Chen
Lingzhou Xue
263
2
0
18 May 2025
Persuasive Calibration
Persuasive Calibration
Yiding Feng
Wei Tang
201
0
0
04 Apr 2025
Conditional Temporal Neural Processes with Covariance Loss
Conditional Temporal Neural Processes with Covariance LossInternational Conference on Machine Learning (ICML), 2025
Boseon Yoo
Jiwoo Lee
Janghoon Ju
Seijun Chung
Soyeon Kim
Jaesik Choi
235
17
0
01 Apr 2025
On the Cone Effect in the Learning Dynamics
On the Cone Effect in the Learning Dynamics
Zhanpeng Zhou
Yongyi Yang
Jie Ren
Mahito Sugiyama
Junchi Yan
336
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20 Mar 2025
Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $μ$P Parametrization
Global Convergence and Rich Feature Learning in LLL-Layer Infinite-Width Neural Networks under μμμP Parametrization
Zixiang Chen
Greg Yang
Qingyue Zhao
Q. Gu
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221
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12 Mar 2025
An Application of the Holonomic Gradient Method to the Neural Tangent
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An Application of the Holonomic Gradient Method to the Neural Tangent Kernel
Akihiro Sakoda
Nobuki Takayama
85
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0
31 Oct 2024
Emergence of Globally Attracting Fixed Points in Deep Neural Networks
  With Nonlinear Activations
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear ActivationsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Amir Joudaki
Thomas Hofmann
MLT
134
0
0
26 Oct 2024
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and OptimizationInternational Joint Conference on Artificial Intelligence (IJCAI), 2024
Xinhao Yao
Hongjin Qian
Xiaolin Hu
Gengze Xu
Wei Liu
Jian Luan
Bin Wang
Wenshu Fan
356
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0
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On the Convergence Analysis of Over-Parameterized Variational
  Autoencoders: A Neural Tangent Kernel Perspective
On the Convergence Analysis of Over-Parameterized Variational Autoencoders: A Neural Tangent Kernel PerspectiveMachine-mediated learning (ML), 2024
Li Wang
Wei Huang
DRL
241
0
0
09 Sep 2024
Implicit Regularization Paths of Weighted Neural Representations
Implicit Regularization Paths of Weighted Neural RepresentationsNeural Information Processing Systems (NeurIPS), 2024
Jin-Hong Du
Pratik Patil
172
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0
28 Aug 2024
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
Ye Qiao
Jingcheng Li
Yifan Zhang
Sitao Huang
362
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DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks
Shrenik Zinage
Sudeepta Mondal
S. Sarkar
225
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30 Jul 2024
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Jingren Liu
Zhong Ji
YunLong Yu
Jiale Cao
Yanwei Pang
Jungong Han
Xuelong Li
CLL
306
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0
24 Jul 2024
Wide stable neural networks: Sample regularity, functional convergence
  and Bayesian inverse problems
Wide stable neural networks: Sample regularity, functional convergence and Bayesian inverse problems
Tomás Soto
198
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The Impact of Initialization on LoRA Finetuning Dynamics
The Impact of Initialization on LoRA Finetuning Dynamics
Soufiane Hayou
Nikhil Ghosh
Bin Yu
AI4CE
199
41
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12 Jun 2024
Equivariant Neural Tangent Kernels
Equivariant Neural Tangent Kernels
Philipp Misof
Pan Kessel
Jan E. Gerken
347
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FedDr+: Stabilizing Dot-regression with Global Feature Distillation for
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FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Seongyoon Kim
Minchan Jeong
Sungnyun Kim
Sungwoo Cho
Sumyeong Ahn
Se-Young Yun
FedML
293
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04 Jun 2024
Regularized Gauss-Newton for Optimizing Overparameterized Neural
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Adeyemi Damilare Adeoye
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Alberto Bemporad
197
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The Positivity of the Neural Tangent Kernel
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José Mourao
Gonccalo Oliveira
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Neural network representation of quantum systems
Neural network representation of quantum systems
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Yuji Hirono
Jun Maeda
Jojiro Totsuka-Yoshinaka
167
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0
18 Mar 2024
Emergent Equivariance in Deep Ensembles
Emergent Equivariance in Deep Ensembles
Jan E. Gerken
Pan Kessel
UQCVMDE
239
13
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05 Mar 2024
LoRA+: Efficient Low Rank Adaptation of Large Models
LoRA+: Efficient Low Rank Adaptation of Large Models
Soufiane Hayou
Nikhil Ghosh
Bin Yu
AI4CE
382
302
0
19 Feb 2024
Flexible Infinite-Width Graph Convolutional Neural Networks
Flexible Infinite-Width Graph Convolutional Neural Networks
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Edward Milsom
Laurence Aitchison
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170
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Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
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Fangzhao Zhang
Mert Pilanci
AI4CE
387
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A note on regularised NTK dynamics with an application to PAC-Bayesian
  training
A note on regularised NTK dynamics with an application to PAC-Bayesian training
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Benjamin Guedj
289
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Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks
Tensor Programs VI: Feature Learning in Infinite-Depth Neural NetworksInternational Conference on Learning Representations (ICLR), 2023
Greg Yang
Dingli Yu
Chen Zhu
Soufiane Hayou
MLT
396
56
0
03 Oct 2023
Commutative Width and Depth Scaling in Deep Neural Networks
Commutative Width and Depth Scaling in Deep Neural Networks
Soufiane Hayou
170
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A Primer on Bayesian Neural Networks: Review and Debates
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Federico Danieli
Konstantinos Pitas
M. Vladimirova
Vincent Fortuin
BDLAAML
227
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Controlling the Inductive Bias of Wide Neural Networks by Modifying the
  Kernel's Spectrum
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
Amnon Geifman
Daniel Barzilai
Ronen Basri
Meirav Galun
272
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Fast Adaptive Test-Time Defense with Robust Features
Fast Adaptive Test-Time Defense with Robust Features
Anurag Singh
Mahalakshmi Sabanayagam
Krikamol Muandet
Debarghya Ghoshdastidar
AAMLTTAOOD
147
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Quantitative CLTs in Deep Neural Networks
Quantitative CLTs in Deep Neural NetworksProbability theory and related fields (PTRF), 2023
Stefano Favaro
Boris Hanin
Domenico Marinucci
I. Nourdin
G. Peccati
BDL
583
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12 Jul 2023
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
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M. Demirtaş
James Halverson
Anindita Maiti
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Keegan Stoner
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173
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How Deep Neural Networks Learn Compositional Data: The Random Hierarchy
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Leonardo Petrini
Umberto M. Tomasini
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Matthieu Wyart
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Efficient Uncertainty Quantification and Reduction for
  Over-Parameterized Neural Networks
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Henry Lam
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Francis R. Bach
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Daniel Fink
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Pretrained Embeddings for E-commerce Machine Learning: When it Fails and
  Why?
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Da Xu
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177
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Wide neural networks: From non-gaussian random fields at initialization
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Phase Diagram of Initial Condensation for Two-layer Neural NetworksCSIAM Transactions on Applied Mathematics (TCAM), 2023
Zheng Chen
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153
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Kernel Regression with Infinite-Width Neural Networks on Millions of
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Ben Adlam
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  towards optimized I-FENN performance
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