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Optimizing Neural Networks with Kronecker-factored Approximate Curvature

19 March 2015

Papers citing "Optimizing Neural Networks with Kronecker-factored Approximate Curvature"

50 / 645 papers shown

Title
Meta-Learning with Variational Bayes Lucas Lingle BDL OOD 49 0 0 03 Mar 2021
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks Apoorva Sharma Navid Azizan Marco Pavone UQCV 91 47 0 24 Feb 2021
Non-Convex Optimization with Spectral Radius Regularization Adam Sandler Diego Klabjan Yuan Luo ODL 60 1 0 22 Feb 2021
Appearance of Random Matrix Theory in Deep Learning Nicholas P. Baskerville Diego Granziol J. Keating 77 11 0 12 Feb 2021
Kronecker-factored Quasi-Newton Methods for Deep Learning Yi Ren Achraf Bahamou Shiqian Ma ODL 29 2 0 12 Feb 2021
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction Yuhang Li Ruihao Gong Xu Tan Yang Yang Peng Hu Qi Zhang F. Yu Wei Wang Shi Gu MQ 181 446 0 10 Feb 2021
A linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li Luca Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 182 49 0 29 Jan 2021
Network Automatic Pruning: Start NAP and Take a Nap Wenyuan Zeng Yuwen Xiong R. Urtasun 55 9 0 17 Jan 2021
An iterative K-FAC algorithm for Deep Learning Yingshi Chen ODL 66 1 0 01 Jan 2021
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy Zedong Tang Fenlong Jiang Junke Song Maoguo Gong Hao Li F. Yu Zidong Wang Min Wang ODL 34 1 0 24 Dec 2020
LQF: Linear Quadratic Fine-Tuning Alessandro Achille Aditya Golatkar Avinash Ravichandran M. Polito Stefano Soatto 120 27 0 21 Dec 2020
Neural Pruning via Growing Regularization Huan Wang Can Qin Yulun Zhang Y. Fu 106 148 0 16 Dec 2020
Study on the Large Batch Size Training of Neural Networks Based on the Second Order Gradient Fengli Gao Huicai Zhong ODL 37 10 0 16 Dec 2020
Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels Yiming Xu Diego Klabjan 65 7 0 08 Dec 2020
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization Adepu Ravi Sankar Yash Khasbage Rahul Vigneswaran V. Balasubramanian 89 44 0 07 Dec 2020
Eigenvalue-corrected Natural Gradient Based on a New Approximation Kai-Xin Gao Xiaolei Liu Zheng-Hai Huang Min Wang Shuangling Wang Zidong Wang Dachuan Xu F. Yu ODL 46 7 0 27 Nov 2020
Reinforcement Learning for Robust Missile Autopilot Design Bernardo Cortez 20 2 0 26 Nov 2020
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient Kai-Xin Gao Xiaolei Liu Zheng-Hai Huang Min Wang Zidong Wang Dachuan Xu F. Yu 65 12 0 21 Nov 2020
A Random Matrix Theory Approach to Damping in Deep Learning Diego Granziol Nicholas P. Baskerville AI4CE ODL 99 2 0 15 Nov 2020
Better, Faster Fermionic Neural Networks J. Spencer David Pfau Aleksandar Botev W. Foulkes 51 49 0 13 Nov 2020
Sinkhorn Natural Gradient for Generative Models Zebang Shen Zhenfu Wang Alejandro Ribeiro Hamed Hassani GAN DiffM 61 12 0 09 Nov 2020
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang Shengyang Sun Roger C. Grosse UQCV 71 25 0 06 Nov 2020
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation Aurick Zhou Sergey Levine BDL OOD UQCV 56 13 0 05 Nov 2020
Reverse engineering learned optimizers reveals known and novel mechanisms Niru Maheswaranathan David Sussillo Luke Metz Ruoxi Sun Jascha Narain Sohl-Dickstein 101 22 0 04 Nov 2020
EAdam Optimizer: How $ε$ Impact Adam Wei Yuan Kai-Xin Gao ODL 41 21 0 04 Nov 2020
Two-Level K-FAC Preconditioning for Deep Learning N. Tselepidis Jonas Köhler Antonio Orvieto ODL 56 2 0 01 Nov 2020
Bayesian Optimization Meets Laplace Approximation for Robotic Introspection Matthias Humt Jongseo Lee Rudolph Triebel BDL UQCV 80 11 0 30 Oct 2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Juhan Bae Roger C. Grosse 73 24 0 26 Oct 2020
Hindsight Experience Replay with Kronecker Product Approximate Curvature M. DhuruvaPriyanG Abhik Singla S. Bhatnagar BDL 23 1 0 09 Oct 2020
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks Yikai Wu Xingyu Zhu Chenwei Wu Annie Wang Rong Ge 122 45 0 08 Oct 2020
A Survey of Deep Meta-Learning Mike Huisman Jan N. van Rijn Aske Plaat 201 335 0 07 Oct 2020
Learnable Uncertainty under Laplace Approximations Agustinus Kristiadi Matthias Hein Philipp Hennig UQCV BDL 76 31 0 06 Oct 2020
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret Ariel Kleiner H. Mobahi Behnam Neyshabur AAML 249 1,363 0 03 Oct 2020
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems Max Mutschler A. Zell 97 0 0 02 Oct 2020
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks Ryo Karakida Kazuki Osawa 82 26 0 02 Oct 2020
Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates Chen Zeno Itay Golan Elad Hoffer Daniel Soudry OOD FedML 102 47 0 01 Oct 2020
Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization Xuezhe Ma ODL 85 32 0 28 Sep 2020
Normalization Techniques in Training DNNs: Methodology, Analysis and Application Lei Huang Jie Qin Yi Zhou Fan Zhu Li Liu Ling Shao AI4CE 176 278 0 27 Sep 2020
Second-order Neural Network Training Using Complex-step Directional Derivative Siyuan Shen Tianjia Shao Kun Zhou Chenfanfu Jiang Feng Luo Yin Yang ODL 27 2 0 15 Sep 2020
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning R. Awasthi K. K. Guliani Saif Ahmad Khan Aniket Vashishtha M. S. Gill Arshita Bhatt A. Nagori Aniket Gupta Ponnurangam Kumaraguru Tavpritesh Sethi 98 24 0 14 Sep 2020
Transform Quantization for CNN (Convolutional Neural Network) Compression Sean I. Young Wang Zhe David S. Taubman B. Girod MQ 119 72 0 02 Sep 2020
Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra Vardan Papyan 64 80 0 27 Aug 2020
Optimization of Graph Neural Networks with Natural Gradient Descent M. Izadi Yihao Fang R. Stevenson Lizhen Lin GNN 136 42 0 21 Aug 2020
Improving predictions of Bayesian neural nets via local linearization Alexander Immer M. Korzepa Matthias Bauer BDL 84 11 0 19 Aug 2020
Whitening and second order optimization both make information in the dataset unusable during training, and can reduce or prevent generalization Neha S. Wadia Daniel Duckworth S. Schoenholz Ethan Dyer Jascha Narain Sohl-Dickstein 104 13 0 17 Aug 2020
AutoSimulate: (Quickly) Learning Synthetic Data Generation Harkirat Singh Behl A. G. Baydin Ran Gal Philip Torr Vibhav Vineet 125 23 0 16 Aug 2020
Natural Reweighted Wake-Sleep Csongor-Huba Várady Riccardo Volpi Luigi Malagò Nihat Ay BDL 112 0 0 15 Aug 2020
Tighter risk certificates for neural networks Maria Perez-Ortiz Omar Rivasplata John Shawe-Taylor Csaba Szepesvári UQCV 97 108 0 25 Jul 2020
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks Alexander Immer BDL 46 4 0 21 Jul 2020
A Differential Game Theoretic Neural Optimizer for Training Residual Networks Guan-Horng Liu T. Chen Evangelos A. Theodorou 59 2 0 17 Jul 2020