Deep Convolutional Networks as shallow Gaussian Processes

16 August 2018

Papers citing "Deep Convolutional Networks as shallow Gaussian Processes"

50 / 76 papers shown

Title
Conditional Temporal Neural Processes with Covariance Loss Boseon Yoo Jiwoo Lee Janghoon Ju Seijun Chung Soyeon Kim Jaesik Choi 70 15 0 01 Apr 2025
Deep Neural Nets as Hamiltonians Mike Winer Boris Hanin 223 0 0 31 Mar 2025
Deep Kernel Posterior Learning under Infinite Variance Prior Weights Jorge Loría A. Bhadra BDL UQCV 66 0 0 02 Oct 2024
Re-evaluating the Advancements of Heterophilic Graph Learning Sitao Luan Qincheng Lu Chenqing Hua Xinyu Wang Jiaqi Zhu Xiao-Wen Chang 70 2 0 09 Sep 2024
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti Stefano Favaro Stefano Peluchetti BDL 234 0 0 26 Aug 2024
Random ReLU Neural Networks as Non-Gaussian Processes Rahul Parhi Pakshal Bohra Ayoub El Biari Mehrsa Pourya Michael Unser 63 1 0 16 May 2024
Neural Redshift: Random Networks are not Random Functions Damien Teney A. Nicolicioiu Valentin Hartmann Ehsan Abbasnejad 103 19 0 04 Mar 2024
On the Neural Tangent Kernel of Equilibrium Models Zhili Feng J. Zico Kolter 23 6 0 21 Oct 2023
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 43 12 0 12 Jul 2023
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models Bo Li Alexandar J. Thomson Matthew M. Engelhard David Page David Page BDL AI4CE 24 0 0 27 May 2023
Do deep neural networks have an inbuilt Occam's razor? Chris Mingard Henry Rees Guillermo Valle Pérez A. Louis UQCV BDL 31 16 0 13 Apr 2023
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities Alberto Bordino Stefano Favaro S. Fortini 28 7 0 08 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 21 2 0 05 Apr 2023
A Simple Algorithm For Scaling Up Kernel Methods Tengyu Xu Bryan Kelly Semyon Malamud 30 0 0 26 Jan 2023
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients Guihong Li Yuedong Yang Kartikeya Bhardwaj R. Marculescu 38 61 0 26 Jan 2023
Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels Kangyu Weng Aohua Cheng Ziyang Zhang Pei Sun Yang Tian 58 2 0 04 Dec 2022
Globally Gated Deep Linear Networks Qianyi Li H. Sompolinsky AI4CE 27 10 0 31 Oct 2022
Evolution of Neural Tangent Kernels under Benign and Adversarial Training Noel Loo Ramin Hasani Alexander Amini Daniela Rus AAML 47 13 0 21 Oct 2022
Uncertainty-Aware Meta-Learning for Multimodal Task Distributions Cesar Almecija Apoorva Sharma Navid Azizan OOD UQCV 26 3 0 04 Oct 2022
Gaussian Process Surrogate Models for Neural Networks Michael Y. Li Erin Grant Thomas Griffiths BDL SyDa 40 7 0 11 Aug 2022
AutoInit: Automatic Initialization via Jacobian Tuning Tianyu He Darshil Doshi Andrey Gromov 27 4 0 27 Jun 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 31 0 0 27 Jun 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 30 54 0 17 Jun 2022
Large-width asymptotics for ReLU neural networks with $α$ -Stable initializations Stefano Favaro S. Fortini Stefano Peluchetti 28 2 0 16 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 48 6 0 15 Jun 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 32 4 0 15 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 32 18 0 30 Apr 2022
Convergence of neural networks to Gaussian mixture distribution Yasuhiko Asao Ryotaro Sakamoto S. Takagi BDL 37 2 0 26 Apr 2022
Ternary and Binary Quantization for Improved Classification Weizhi Lu Mingrui Chen Kai Guo Weiyu Li MQ 20 0 0 31 Mar 2022
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin UQCV 45 10 0 28 Feb 2022
Multi-model Ensemble Analysis with Neural Network Gaussian Processes Trevor Harris Yangqiu Song Ryan Sriver 27 5 0 08 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 39 3 0 30 Jan 2022
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication François Bachoc A. Lagnoux 34 4 0 14 Dec 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 18 2 0 30 Oct 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 54 9 0 06 Oct 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 57 442 0 19 Aug 2021
Nonperturbative renormalization for the neural network-QFT correspondence Harold Erbin Vincent Lahoche D. O. Samary 46 30 0 03 Aug 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 51 231 0 27 Jul 2021
Random Neural Networks in the Infinite Width Limit as Gaussian Processes Boris Hanin BDL 37 44 0 04 Jul 2021
Precise characterization of the prior predictive distribution of deep ReLU networks Lorenzo Noci Gregor Bachmann Kevin Roth Sebastian Nowozin Thomas Hofmann BDL UQCV 31 32 0 11 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 38 124 0 14 May 2021
Quantum field-theoretic machine learning Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 19 28 0 18 Feb 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 29 250 0 12 Feb 2021
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction Dominik Narnhofer Alexander Effland Erich Kobler Kerstin Hammernik Florian Knoll Thomas Pock UQCV BDL 23 49 0 12 Feb 2021
Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen Zhourung Chen Jaehoon Lee DD 36 241 0 30 Oct 2020
Tensor Programs III: Neural Matrix Laws Greg Yang 19 45 0 22 Sep 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 37 199 0 22 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 46 100 0 15 Jun 2020
NP-PROV: Neural Processes with Position-Relevant-Only Variances Xuesong Wang Lina Yao Xianzhi Wang Feiping Nie BDL 26 3 0 15 Jun 2020