Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

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Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

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 ...

What changes with age? Results in reinforcement learning (RL) are mixed. Some studies found deteriorated RL performance in older participants compared to younger controls whereas other studies did not. Daniel et al. (J Neurosci 40(5):1084–1096, 2020. 10.15 ...

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 ...

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 ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

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 ...

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 ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the 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 ...

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically ...

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 ...

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation ...

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 ...

The channel impulse response (CIR) of the wireless propagation channel is influenced by the surrounding environment and thus can be used to retrieve environmental information such as location, the presence of objects, and the speed of objects. In this work ...

Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynam ...

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 ...

Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

Chattez avec Graph Search

Sequential Reasoning with Socially Caused Beliefs

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

Intact reinforcement learning in healthy ageing

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

Learning to Reason with Neural Networks: Principles and Methods

Person Re-Identification and its Application to Multi-Object Tracking

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

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

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

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

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

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

Understanding generalization and robustness in modern deep learning

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

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

Towards the Detection of AI-Synthesized Human Face Images

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

Learning-based Hand Gesture Classification using Channel Impulse Response with UWB

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

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