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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 126–150 of 181 papers

TitleStatusHype
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methodsCode0
Curvature-corrected learning dynamics in deep neural networks—0
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic RatesCode0
Hierarchical model-based policy optimization: from actions to action sequences and back—0
Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow—0
Fast and Furious Convergence: Stochastic Second Order Methods under InterpolationCode0
EXACT ANALYSIS OF CURVATURE CORRECTED LEARNING DYNAMICS IN DEEP LINEAR NETWORKS—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Practical Newton-Type Distributed Learning using Gradient Based Approximations—0
Meta-descent for Online, Continual Prediction—0
SGD momentum optimizer with step estimation by online parabola modelCode0
Limitations of the Empirical Fisher Approximation for Natural Gradient DescentCode0
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems—0
LIBS2ML: A Library for Scalable Second Order Machine Learning AlgorithmsCode0
Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directionsCode0
Stochastic Trust Region Inexact Newton Method for Large-scale Machine LearningCode0
Bilinear Parameterization For Differentiable Rank-Regularization—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Large batch size training of neural networks with adversarial training and second-order informationCode0
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient—0
A Distributed Second-Order Algorithm You Can Trust—0
Online Second Order Methods for Non-Convex Stochastic Optimizations—0
GPU Accelerated Sub-Sampled Newton's Method—0
The Many Faces of Exponential Weights in Online Learning—0
A comparison of second-order methods for deep convolutional neural networks—0
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