Neural Tanget Kernel: Convergence and Generalization in Neural Networks

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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Neural Tanget Kernel: Convergence and Generalization in Neural Networks

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Partial discharge localization in power transformer tanks using machine learning methods

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Secure and federated genome-wide association studies for biobank-scale datasets

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Gibbs sampling the posterior of neural networks

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Fair Decentralized Learning

Optimizing Dynamic Aperture Studies with Active Learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Enabling Uncertainty Estimation in Iterative Neural Networks

A learning-based approach to stochastic optimal control under reach-avoid constraint

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

The neural correlates of topographical disorientation-a lesion analysis study

Factor-graph Optimisation with Vehicle Dynamic Models for Mapping & Sensing Applications