Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

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Computational methods at the atomic scale based on machine learning have been a fruitful research field in the past decade by merging the advantages of classical and quantum mechanical approaches. While parts of the field are already reaching a mature stat ...

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In recent years, numerical simulations of hemodynamics have gained significant attention within the medical community, thanks to their ability of non-invasively estimating the blood flow conditions. However, high-fidelity simulations require extensive comp ...

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Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

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Reverse engineering primate sensorimotor control with machine learning

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

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Partial annotations in active learning for semantic segmentation

Learning Time-Scales in Two-Layers Neural Networks

Neurostimulation and Wireless Data Transmission Circuits for Implantable High-Density Neural Interfaces

Geometry-Driven Stock-Constrained Truss Design via Equilibrium-Based Structural Models

Universality laws for Gaussian mixtures in generalized linear models

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Cellpose training data and scripts from "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments"

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

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Event-driven acquisition for content-enriched microscopy

Complex Analysis meets Statistical Mechanics: Applications to Kernel Methods and Conformal Field Theory

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