Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

In this work, we develop first-order (Hessian-free) and zeroth-order (derivative-free) implementations of the Cubically Regularized Newton Method for solving general non-convex optimization problems. For that, we employ finite difference approximations of ...

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Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

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Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

First and Zeroth-Order Implementations of the Regularized Newton Method with Lazy Approximated Hessians

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Efficient and Accurate Handling of Periodic Flows in Time-Sensitive Networks

Image steganalysis using active learning and hyperparameter optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Dimensionless Group-Driven Ensemble Neural Networks for Robust Design Optimisation in Engineering

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Enhancing skin lesion classification: a CNN approach with human baseline comparison

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

On the added value of sequential deep learning for the upscaling of evapotranspiration