Gauss–Newton algorithm
PulseAugur coverage of Gauss–Newton algorithm — every cluster mentioning Gauss–Newton algorithm across labs, papers, and developer communities, ranked by signal.
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New research explores Gauss-Newton accuracy and indefinite Hessians in low-cost sets
This paper delves into the accuracy of the Gauss-Newton curvature within ridge-regularized nonlinear least squares. It establishes that under specific conditions of local regularity and level-set curvature persistence, …
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New Gauss-Newton method optimizes hyperparameters for AI models
Researchers have developed a novel multi-objective hyperparameter optimization method based on a damped Gauss-Newton approach. This technique treats hyperparameter tuning as a numerical optimization problem, estimating …
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New algorithm accelerates solving large-scale nonlinear least-squares problems
Researchers have developed a new Adaptive Hybrid Subspace Levenberg-Marquardt (HSLM) algorithm designed to efficiently solve large-scale nonlinear least-squares problems. This method constructs a low-dimensional subspac…
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New Advective Fisher-Rao Metric Enhances Probability Measure Optimization
Researchers have introduced a new advective Fisher-Rao metric designed for optimization tasks involving probability measures governed by the continuity equation. This metric has been demonstrated to provide optimal desc…
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Neural operator learns collision-free spacecraft swarm trajectories · 2 sources tracked
Researchers have developed a novel neural operator that can plan collision-free trajectories for large swarms of spacecraft. This operator maps spacecraft, target, and debris distributions to trajectories in a single fo…
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New Geometric Observability Index Enhances SE(3) Pose Estimation
Researchers have introduced the Geometric Observability Index (GOI), a novel metric for assessing the sensitivity of pose estimation in SE(3) environments. This index quantifies the influence of individual measurements …
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New framework boosts physics-informed neural network accuracy
Researchers have developed DSGNAR, a novel optimization framework designed to improve the training of physics-informed neural networks (PINNs). This framework addresses the ill-conditioning issues that have previously l…
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New BREIT framework enhances brain stroke reconstruction with 3D EIT
Researchers have developed BREIT, a new framework designed to improve brain stroke reconstruction using Multi-Frequency Electrical Impedance Tomography (MF-EIT). This framework addresses limitations in current 3D deep-l…
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Paper explores preconditioned gradient descent's impact on neural network learning regimes
This paper investigates how preconditioned gradient descent (PGD) methods, like Gauss-Newton, influence spectral bias and the phenomenon of grokking in neural networks. Researchers propose that PGD can mitigate spectral…