Enhancing Discrete Choice Models with Neural Networks

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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 ...

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Enhancing Discrete Choice Models with Neural Networks

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Learning to Reason with Neural Networks: Principles and Methods

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

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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Computational design of complex protein folds and soluble analogs of membrane proteins

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Sparse autoregressive neural networks for classical spin systems

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Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting

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Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion

Enhancement of Ultrafast Ultrasound Images: A Performance Comparison Between CNN Trained with RF or IQ Images

Asymptotics of feature learning in two-layer networks after one gradient-step

Understanding generalization and robustness in modern deep learning