Sparse Polynomial Chaos expansions using variational relevance vector machines

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Sparse Polynomial Chaos expansions using variational relevance vector machines

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Topics in statistical physics of high-dimensional machine learning

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Random matrix methods for high-dimensional machine learning models

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Pseudo-Three-Dimensional Analytical Model of Linear Induction Motors for High-Speed Applications

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Stochastic Simulations and Machine Learning Modeling to Predict Bedload Transport and Bed Topography

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

On the Generalization of Stochastic Gradient Descent with Momentum

Understanding generalization and robustness in modern deep learning

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

The time-domain Cartesian multipole expansion of electromagnetic fields

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Reliable data-driven decision-making through optimal transport

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Towards Trustworthy Deep Learning for Image Reconstruction

FENNECS: a novel particle-in-cell code for simulating the formation of magnetized non-neutral plasmas trapped by electrodes of complex geometries

Efficient and insightful descriptors for representing molecular and material space