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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
Investigating Generalization by Controlling Normalized Margin Alexander R. Farhang Jeremy Bernstein Kushal Tirumala Yang Liu Yisong Yue 90 6 0 08 May 2022
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization Vahid Keshavarzzadeh Robert M. Kirby A. Narayan BDL 69 2 0 07 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 158 19 0 30 Apr 2022
Deep Ensemble as a Gaussian Process Approximate Posterior Zhijie Deng Feng Zhou Jianfei Chen Guoqiang Wu Jun Zhu UQCV 58 5 0 30 Apr 2022
Convergence of neural networks to Gaussian mixture distribution Yasuhiko Asao Ryotaro Sakamoto S. Takagi BDL 75 2 0 26 Apr 2022
On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 96 13 0 22 Apr 2022
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees Wenying Deng Beau Coker Rajarshi Mukherjee J. Liu B. Coull 65 2 0 15 Apr 2022
Single-level Adversarial Data Synthesis based on Neural Tangent Kernels Yu-Rong Zhang Ruei-Yang Su Sheng-Yen Chou Shan Wu GAN 151 2 0 08 Apr 2022
Analytic theory for the dynamics of wide quantum neural networks Junyu Liu K. Najafi Kunal Sharma F. Tacchino Liang Jiang Antonio Mezzacapo 92 53 0 30 Mar 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes Elvis Dohmatob A. Bietti AAML 112 13 0 22 Mar 2022
Origami in N dimensions: How feed-forward networks manufacture linear separability Christian Keup M. Helias 81 8 0 21 Mar 2022
A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller Viktor Zaverkin Johannes Kastner Ingo Steinwart UQCV BDL GP 145 37 0 17 Mar 2022
Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels Mengyang Gu Xubo Liu X. Fang Sui Tang 69 8 0 16 Mar 2022
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel Chi-Ken Lu Patrick Shafto BDL 101 0 0 14 Mar 2022
Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks Andrea Basteri Dario Trevisan BDL 82 21 0 14 Mar 2022
Deep Regression Ensembles Antoine Didisheim Bryan Kelly Semyon Malamud UQCV 61 4 0 10 Mar 2022
Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process S. Sun Bailu Jin Zhuangkun Wei Weisi Guo 56 3 0 09 Mar 2022
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Greg Yang J. E. Hu Igor Babuschkin Szymon Sidor Xiaodong Liu David Farhi Nick Ryder J. Pachocki Weizhu Chen Jianfeng Gao 158 168 0 07 Mar 2022
Generalization Through The Lens Of Leave-One-Out Error Gregor Bachmann Thomas Hofmann Aurelien Lucchi 142 8 0 07 Mar 2022
An Analysis of Ensemble Sampling Chao Qin Zheng Wen Xiuyuan Lu Benjamin Van Roy 135 22 0 02 Mar 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 139 28 0 01 Mar 2022
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality Chandrashekar Lakshminarayanan Ashutosh Kumar Singh A. Rajkumar AI4CE 82 1 0 01 Mar 2022
Embedded Ensembles: Infinite Width Limit and Operating Regimes Maksim Velikanov Roma Kail Ivan Anokhin Roman Vashurin Maxim Panov Alexey Zaytsev Dmitry Yarotsky 56 1 0 24 Feb 2022
A duality connecting neural network and cosmological dynamics Sven Krippendorf M. Spannowsky AI4CE 83 8 0 22 Feb 2022
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography Francisca Vasconcelos Bobby He Nalini Singh Yee Whye Teh BDL OOD UQCV 84 13 0 22 Feb 2022
Learning from Randomly Initialized Neural Network Features Ehsan Amid Rohan Anil W. Kotłowski Manfred K. Warmuth MLT 85 15 0 13 Feb 2022
Decomposing neural networks as mappings of correlation functions Kirsten Fischer Alexandre René Christian Keup Moritz Layer David Dahmen M. Helias FAtt 107 15 0 10 Feb 2022
Multi-model Ensemble Analysis with Neural Network Gaussian Processes Trevor Harris Yangqiu Song Ryan Sriver 137 5 0 08 Feb 2022
Lossy Gradient Compression: How Much Accuracy Can One Bit Buy? Sadaf Salehkalaibar Stefano Rini FedML 68 4 0 06 Feb 2022
Learning Representation from Neural Fisher Kernel with Low-rank Approximation Ruixiang Zhang Shuangfei Zhai Etai Littwin J. Susskind SSL 92 3 0 04 Feb 2022
Deep Layer-wise Networks Have Closed-Form Weights Chieh-Tsai Wu A. Masoomi Arthur Gretton Jennifer Dy 71 3 0 01 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 105 3 0 30 Jan 2022
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis Shuai Zhang Ming Wang Sijia Liu Pin-Yu Chen Jinjun Xiong SSL MLT 124 23 0 21 Jan 2022
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions Mojtaba Sahraee-Ardakan M. Emami Parthe Pandit S. Rangan A. Fletcher 109 9 0 20 Jan 2022
Adaptive Transfer Learning for Plant Phenotyping Jun Wu Elizabeth Ainsworth Sheng Wang K. Guan Jingrui He 13 2 0 14 Jan 2022
On neural network kernels and the storage capacity problem Jacob A. Zavatone-Veth Cengiz Pehlevan 63 6 0 12 Jan 2022
Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks Shaun Li AI4CE 84 0 0 03 Jan 2022
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs Inbar Seroussi Gadi Naveh Zohar Ringel 113 55 0 31 Dec 2021
NN2Poly: A polynomial representation for deep feed-forward artificial neural networks Pablo Morala Jenny Alexandra Cifuentes R. Lillo Iñaki Ucar 113 7 0 21 Dec 2021
Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective Simon Heilig Maximilian Münch Frank-Michael Schleif 45 1 0 18 Dec 2021
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication François Bachoc A. Lagnoux 89 4 0 14 Dec 2021
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks Lechao Xiao 118 22 0 10 Dec 2021
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity Junchi Yang Antonio Orvieto Aurelien Lucchi Niao He 117 64 0 10 Dec 2021
Provable Continual Learning via Sketched Jacobian Approximations Reinhard Heckel CLL 87 10 0 09 Dec 2021
Infinite Neural Network Quantum States: Entanglement and Training Dynamics Di Luo James Halverson 79 6 0 01 Dec 2021
Dependence between Bayesian neural network units M. Vladimirova Julyan Arbel Stéphane Girard BDL 110 3 0 29 Nov 2021
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping Xuran Meng Jianfeng Yao 98 7 0 26 Nov 2021
Critical Initialization of Wide and Deep Neural Networks through Partial Jacobians: General Theory and Applications Darshil Doshi Tianyu He Andrey Gromov 95 10 0 23 Nov 2021
Depth induces scale-averaging in overparameterized linear Bayesian neural networks Jacob A. Zavatone-Veth Cengiz Pehlevan BDL UQCV MDE 106 11 0 23 Nov 2021
On the Equivalence between Neural Network and Support Vector Machine Yilan Chen Wei Huang Lam M. Nguyen Tsui-Wei Weng AAML 93 18 0 11 Nov 2021