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 ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Object detection is crucial in video analytics pipelines, but there is a need to optimize deep neural networks (DNNs)-based object detection for resource-constrained Internet of Things (IoT) devices. The computational constraints inherent to the IoT device ...

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle's motion remains bounded over a finite number of turns. Such a ...

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface compared to black- ...

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control f ...

Ultrafast ultrasound imaging achieves high frame rates but results in lower image quality compared to focused acquisitions. Convolutional neural networks (CNNs) have emerged as effective tools for reducing diffraction artifacts and enhancing the quality of ...

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

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

High-performance deep spiking neural networks with 0.3 spikes per neuron

Using Physical Dynamics: Accurate and Real-Time Object Detection for High-Resolution Video Streaming on Internet of Things Devices

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

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

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes

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