Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
Monte Carlo sampling methods using Markov chains and their applications
3 Pith papers cite this work. Polarity classification is still indexing.
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A centered swept-back multipolar magnetic field up to octupole order reproduces the bolometric thermal X-ray light curve of MSP J0030+0451.
Bayesian neural networks enable farm-wide virtual load monitoring by predicting structural loads on non-instrumented offshore wind turbines from a fleet-leader's data while quantifying prediction uncertainty.
citing papers explorer
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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The swept-back multipolar magnetic field of neutron stars: Application to NICER MSP J0030+0451
A centered swept-back multipolar magnetic field up to octupole order reproduces the bolometric thermal X-ray light curve of MSP J0030+0451.
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Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
Bayesian neural networks enable farm-wide virtual load monitoring by predicting structural loads on non-instrumented offshore wind turbines from a fleet-leader's data while quantifying prediction uncertainty.