Second-order methods
23 papers with code • 0 benchmarks • 0 datasets
Use second-order statistics to process data.
Benchmarks
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Most implemented papers
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
We introduce ADAHESSIAN, a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the HESSIAN.
Second-Order Stochastic Optimization for Machine Learning in Linear Time
First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity.
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization
In this work we motivate the extension of Newton methods to the SA regime, and argue for the use of the scalable low rank saddle free Newton (LRSFN) method, which avoids forming the Hessian in favor of making a low rank approximation.
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
blupblupblup/near-ood-doppler-signatures
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We emphasize the relevance of OOD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems.
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
GCaptainNemo/optimization-project
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We then discuss some of the distinctive features of these optimization problems, focusing on the examples of logistic regression and the training of deep neural networks.
Online Second Order Methods for Non-Convex Stochastic Optimizations
lixilinx/psgd_tf
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This paper proposes a family of online second order methods for possibly non-convex stochastic optimizations based on the theory of preconditioned stochastic gradient descent (PSGD), which can be regarded as an enhance stochastic Newton method with the ability to handle gradient noise and non-convexity simultaneously.
Large batch size training of neural networks with adversarial training and second-order information
amirgholami/hessianflow
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ICLR 2019
Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively).
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems.
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i. e., big data problems in machine learning.
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order information.
