Learning Network Structures from Firing Patterns

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This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

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Accurate segmentation of anatomical substructures within 3D curvilinear structures in medical imaging remains challenging due to their complex geometry and the scarcity of diverse, large-scale datasets for algorithm development and evaluation. In this pape ...

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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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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

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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 ...

Learning Network Structures from Firing Patterns

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Learning to Reason with Neural Networks: Principles and Methods

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

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Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

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Fine-grained mapping of neural progenitors subtypes in the embryonic brain unveils context-dependent roles of signalling on cell fate decisions

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Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Aggregating Spatial and Photometric Context for Photometric Stereo

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Optimizing Dynamic Aperture Studies with Active Learning