On the robustness of randomized classifiers to adversarial examples

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Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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On the robustness of randomized classifiers to adversarial examples

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Optimizing Dynamic Aperture Studies with Active Learning

On the number of regions of piecewise linear neural networks

Random matrix methods for high-dimensional machine learning models

Incorporating Projective Geometry into Deep Learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Machine Learning for Modeling Stock Returns

Partial discharge localization in power transformer tanks using machine learning methods

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Safe Deep Neural Networks

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

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Topics in statistical physics of high-dimensional machine learning

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Network time series forecasting in photovoltaics power production

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Gibbs sampling the posterior of neural networks

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging