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Deep Neural Networks as Gaussian Processes

1 November 2017

Jascha Narain Sohl-Dickstein

UQCV

BDL

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Papers citing "Deep Neural Networks as Gaussian Processes"

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Title
A theory of data variability in Neural Network Bayesian inference Javed Lindner David Dahmen Michael Krämer M. Helias BDL 83 3 0 31 Jul 2023
Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks R. Aiudi R. Pacelli A. Vezzani R. Burioni P. Rotondo MLT 91 16 0 21 Jul 2023
Dataset Distillation Meets Provable Subset Selection M. Tukan Alaa Maalouf Margarita Osadchy DD 88 4 0 16 Jul 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs T. Kim T. Can K. Krishnamurthy AI4CE 91 5 0 12 Jul 2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks Inbar Seroussi Asaf Miron Zohar Ringel PINN 115 0 0 12 Jul 2023
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 164 16 0 12 Jul 2023
Fundamental limits of overparametrized shallow neural networks for supervised learning Francesco Camilli D. Tieplova Jean Barbier 81 10 0 11 Jul 2023
Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities Guihong Li Duc-Tuong Hoang Kartikeya Bhardwaj Ming Lin Zhangyang Wang R. Marculescu 140 15 0 05 Jul 2023
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen 102 1 0 03 Jul 2023
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit Lorenzo Noci Chuning Li Mufan Li Bobby He Thomas Hofmann Chris J. Maddison Daniel M. Roy 135 36 0 30 Jun 2023
Introspective Perception for Mobile Robots Sadegh Rabiee Joydeep Biswas 84 4 0 29 Jun 2023
Gaussian random field approximation via Stein's method with applications to wide random neural networks Krishnakumar Balasubramanian L. Goldstein Nathan Ross Adil Salim 133 9 0 28 Jun 2023
Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data Wenbo Ge Pooia Lalbakhsh Leigh Isai Artem Lenskiy Hanna Suominen OOD 43 3 0 20 Jun 2023
Representation and decomposition of functions in DAG-DNNs and structural network pruning Wonjun Hwang 57 1 0 16 Jun 2023
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning Daniel Huang Christian Camaño Jonathan Tsegaye Jonathan Austin Gale AI4CE 86 0 0 10 Jun 2023
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks Ziyi Huang Henry Lam Haofeng Zhang UQCV 86 7 0 09 Jun 2023
Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable Widths Yuesheng Xu Haizhang Zhang 151 3 0 02 Jun 2023
Centered Self-Attention Layers Ameen Ali Tomer Galanti Lior Wolf 144 8 0 02 Jun 2023
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization Navid Ansari Hans-Peter Seidel Vahid Babaei 95 2 0 01 Jun 2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Y. Li Tim G. J. Rudner A. Wilson BDL 114 34 0 31 May 2023
A Rainbow in Deep Network Black Boxes Florentin Guth Brice Ménard G. Rochette S. Mallat 119 12 0 29 May 2023
An Improved Variational Approximate Posterior for the Deep Wishart Process Sebastian W. Ober Ben Anson Edward Milsom Laurence Aitchison BDL 55 5 0 23 May 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 121 14 0 23 May 2023
Squared Neural Families: A New Class of Tractable Density Models Russell Tsuchida Cheng Soon Ong Dino Sejdinovic TPM 78 12 0 22 May 2023
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance Jorge Loría A. Bhadra UQCV BDL 146 1 0 18 May 2023
Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks Chanwoo Chun Daniel D. Lee BDL 89 2 0 17 May 2023
A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks Ali Gorji Andisheh Amrollahi A. Krause 55 4 0 16 May 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 84 18 0 08 May 2023
Structures of Neural Network Effective Theories cCaugin Ararat Tianji Cai Cem Tekin Zhengkang Zhang 117 7 0 03 May 2023
Random Function Descent Felix Benning L. Döring 57 0 0 02 May 2023
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability Sitao Luan Chenqing Hua Minkai Xu Qincheng Lu Jiaqi Zhu Xiaoming Chang Jie Fu J. Leskovec Doina Precup 103 5 0 25 Apr 2023
Generalization and Estimation Error Bounds for Model-based Neural Networks Avner Shultzman Eyar Azar M. Rodrigues Yonina C. Eldar 68 7 0 19 Apr 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 95 10 0 17 Apr 2023
Deep neural networks have an inbuilt Occam's razor Chris Mingard Henry Rees Guillermo Valle Pérez A. Louis UQCV BDL 104 16 0 13 Apr 2023
UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts Pratinav Seth Mihir Agarwal AI4MH 71 1 0 10 Apr 2023
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities Alberto Bordino Stefano Favaro S. Fortini 89 8 0 08 Apr 2023
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks Blake Bordelon Cengiz Pehlevan MLT 115 31 0 06 Apr 2023
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training Luís Carvalho Joao L. Costa José Mourao Gonccalo Oliveira AI4CE 72 2 0 06 Apr 2023
Biological Sequence Kernels with Guaranteed Flexibility Alan N. Amin Eli N. Weinstein D. Marks 90 4 0 06 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 69 2 0 05 Apr 2023
Effective Theory of Transformers at Initialization Emily Dinan Sho Yaida Susan Zhang 100 16 0 04 Apr 2023
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution Jaeill Kim Suhyun Kang Duhun Hwang Jungwook Shin Wonjong Rhee DRL 109 24 0 04 Apr 2023
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes Yifan Chen H. Owhadi F. Schafer 102 31 0 03 Apr 2023
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting Timm Hess Eli Verwimp Gido M. van de Ven Tinne Tuytelaars CLL 128 9 0 03 Apr 2023
Neural signature kernels as infinite-width-depth-limits of controlled ResNets Nicola Muca Cirone M. Lemercier C. Salvi 95 25 0 30 Mar 2023
Inferring networks from time series: a neural approach Thomas Gaskin G. Pavliotis Mark Girolami AI4TS 89 7 0 30 Mar 2023
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control Tyler Lekang Andrew G. Lamperski 79 0 0 28 Mar 2023
On the Stepwise Nature of Self-Supervised Learning James B. Simon Maksis Knutins Liu Ziyin Daniel Geisz Abraham J. Fetterman Joshua Albrecht SSL 104 35 0 27 Mar 2023
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle Rylan Schaeffer Mikail Khona Zachary Robertson Akhilan Boopathy Kateryna Pistunova J. Rocks Ila Rani Fiete Oluwasanmi Koyejo 156 35 0 24 Mar 2023
Online Learning for the Random Feature Model in the Student-Teacher Framework Roman Worschech B. Rosenow 97 0 0 24 Mar 2023