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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 51–100 of 181 papers

TitleStatusHype
Alternating Iteratively Reweighted _1 and Subspace Newton Algorithms for Nonconvex Sparse OptimizationCode0
SGD momentum optimizer with step estimation by online parabola modelCode0
SGD with Partial Hessian for Deep Neural Networks OptimizationCode0
Sharpened Lazy Incremental Quasi-Newton MethodCode0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm—0
First and Second Order Methods for Online Convolutional Dictionary Learning—0
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning—0
GPU Accelerated Sub-Sampled Newton's Method—0
Gradient-Boosted Based Structured and Unstructured Learning—0
Gradient Norm Regularization Second-Order Algorithms for Solving Nonconvex-Strongly Concave Minimax Problems—0
Hierarchical model-based policy optimization: from actions to action sequences and back—0
Highly Efficient Hierarchical Online Nonlinear Regression Using Second Order Methods—0
Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow—0
Improving Stochastic Cubic Newton with Momentum—0
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning—0
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization—0
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks—0
Kronecker-factored Quasi-Newton Methods for Deep Learning—0
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate—0
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method—0
Learning-Augmented Sketches for Hessians—0
Meta-descent for Online, Continual Prediction—0
Minibatching Offers Improved Generalization Performance for Second Order Optimizers—0
Mirror Prox Algorithm for Large-Scale Cell-Free Massive MIMO Uplink Power Control—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Nesterov's Acceleration For Approximate Newton—0
Nestrov's Acceleration For Second Order Method—0
Newton methods based convolution neural networks using parallel processing—0
Newton-Stein Method: An optimization method for GLMs via Stein's Lemma—0
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma—0
On backpropagating Hessians through ODEs—0
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning—0
On the importance of initialization and momentum in deep learning—0
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks—0
Oracle Complexity of Second-Order Methods for Finite-Sum Problems—0
Practical Newton-Type Distributed Learning using Gradient Based Approximations—0
Preconditioners for the Stochastic Training of Neural Fields—0
Provable Regret Bounds for Deep Online Learning and Control—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Quasi-Newton Quasi-Monte Carlo for variational Bayes—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training—0
RES: Regularized Stochastic BFGS Algorithm—0
Saddle-free Hessian-free Optimization—0
SCORE: Approximating Curvature Information under Self-Concordant Regularization—0
Second Order Bilinear Discriminant Analysis for single trial EEG analysis—0
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