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1711.00165
Cited By
Deep Neural Networks as Gaussian Processes
1 November 2017
Jaehoon Lee
Yasaman Bahri
Roman Novak
S. Schoenholz
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
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Papers citing
"Deep Neural Networks as Gaussian Processes"
50 / 692 papers shown
Title
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Precise Asymptotic Analysis of Deep Random Feature Models
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Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
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M. Anitescu
Jing Chen
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27
4
0
12 Feb 2023
Gaussian Process-Gated Hierarchical Mixtures of Experts
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Marzieh Ajirak
Petar M. Djurić
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1
0
09 Feb 2023
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
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Ming Wang
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Miaoyuan Liu
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29
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0
06 Feb 2023
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
François Caron
Fadhel Ayed
Paul Jung
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Hongseok Yang
67
2
0
02 Feb 2023
Width and Depth Limits Commute in Residual Networks
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Greg Yang
50
14
0
01 Feb 2023
Deterministic equivalent and error universality of deep random features learning
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Hugo Cui
Daniil Dmitriev
Bruno Loureiro
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39
28
0
01 Feb 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width
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Florent Krzakala
Lenka Zdeborová
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30
35
0
01 Feb 2023
Gradient Descent in Neural Networks as Sequential Learning in RKBS
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Sunil R. Gupta
Santu Rana
Svetha Venkatesh
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24
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01 Feb 2023
Deep networks for system identification: a Survey
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Antônio H. Ribeiro
Thomas B. Schon
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42
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0
30 Jan 2023
A Simple Algorithm For Scaling Up Kernel Methods
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Bryan Kelly
Semyon Malamud
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Neural networks learn to magnify areas near decision boundaries
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Cengiz Pehlevan
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Estimating Causal Effects using a Multi-task Deep Ensemble
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Zhuoran Hou
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Yiman Ren
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David Carlson
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26 Jan 2023
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion
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Stefano Rini
Sadaf Salehkalaibar
Jun Chen
FedML
21
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23 Jan 2023
Towards Quantification of Assurance for Learning-enabled Components
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E. Denney
Ganesh J. Pai
18
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Catapult Dynamics and Phase Transitions in Quadratic Nets
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Junyu Liu
29
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0
18 Jan 2023
Dataset Distillation: A Comprehensive Review
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Xinchao Wang
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121
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Smooth Mathematical Function from Compact Neural Networks
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26
0
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31 Dec 2022
Bayesian Interpolation with Deep Linear Networks
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Alexander Zlokapa
49
25
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29 Dec 2022
Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels
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Aohua Cheng
Ziyang Zhang
Pei Sun
Yang Tian
58
2
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Ashish Mahabal
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K. Polsterer
A. Krone-Martins
26
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Diffusion Probabilistic Model Made Slim
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DiffM
29
104
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A Kernel Perspective of Skip Connections in Convolutional Networks
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Amnon Geifman
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23
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Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth
Filipe de Avila Belbute-Peres
J. Zico Kolter
32
2
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26 Nov 2022
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data
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Zhiqi Bu
Q. Long
35
4
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Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
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Arkil Patel
Varun Kanade
Phil Blunsom
22
46
0
22 Nov 2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Jiayu Yao
Yaniv Yacoby
Beau Coker
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Finale Doshi-Velez
24
1
0
16 Nov 2022
Characterizing the Spectrum of the NTK via a Power Series Expansion
Michael Murray
Hui Jin
Benjamin Bowman
Guido Montúfar
43
11
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Spectral Evolution and Invariance in Linear-width Neural Networks
Zhichao Wang
A. Engel
Anand D. Sarwate
Ioana Dumitriu
Tony Chiang
45
14
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Overparameterized random feature regression with nearly orthogonal data
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Yizhe Zhu
31
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Adil Mehmood Khan
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Saurabh Mishra
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31
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Globally Gated Deep Linear Networks
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H. Sompolinsky
AI4CE
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10
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A Solvable Model of Neural Scaling Laws
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Daniel A. Roberts
J. Sully
52
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S. L. Alarcón
Cory E. Merkel
Martin Hoffnagle
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Alejandro Pozas-Kerstjens
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Efficient Dataset Distillation Using Random Feature Approximation
Noel Loo
Ramin Hasani
Alexander Amini
Daniela Rus
DD
81
98
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Evolution of Neural Tangent Kernels under Benign and Adversarial Training
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Ramin Hasani
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Daniela Rus
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44
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Global Convergence of SGD On Two Layer Neural Nets
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Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
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Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
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36
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TiDAL: Learning Training Dynamics for Active Learning
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Hyeongmin Byun
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Meta-Principled Family of Hyperparameter Scaling Strategies
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58
16
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Alexandros Iosifidis
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34
1
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The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks
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Cengiz Pehlevan
41
22
0
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Uncertainty-Aware Meta-Learning for Multimodal Task Distributions
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Apoorva Sharma
Navid Azizan
OOD
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26
3
0
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35
22
0
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Randall Balestriero
Yubei Chen
S. Lloyd
Yann LeCun
SSL
51
13
0
29 Sep 2022
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