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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
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks—0
The Many Faces of Exponential Weights in Online Learning—0
Unlocking FedNL: Self-Contained Compute-Optimized Implementation—0
Utility Maximization for Large-Scale Cell-Free Massive MIMO Downlink—0
When Does Preconditioning Help or Hurt Generalization?—0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
A block coordinate descent optimizer for classification problems exploiting convexity—0
Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix—0
Accelerating Stochastic Probabilistic Inference—0
A Chaos Theory Approach to Understand Neural Network Optimization—0
A comparison of second-order methods for deep convolutional neural networks—0
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization—0
A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods—0
Adaptive Optimization Algorithms for Machine Learning—0
Adaptive Second Order Coresets for Data-efficient Machine Learning—0
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time—0
A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization—0
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training—0
A Distributed Second-Order Algorithm You Can Trust—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching—0
A Generic Approach for Escaping Saddle points—0
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems—0
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