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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 76–100 of 181 papers

TitleStatusHype
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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