Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

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This thesis aimed to estimate the effect of demographic and biomechanical preoperative variables on total shoulder arthroplasty (TSA) outcomes. To achieve this, various biomechanical variables were calculated using a clinical database comprising normal and ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Electric vehicles (EVs) currently dominate the sales in the automotive market. A big leap in this market can be made by developing a photovoltaic product that can be integrated to an EV, as it can boost the driving range of the EV while reducing the chargi ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

This thesis presents a comprehensive theory of information-theoretic measures for characterizing the complexity and higher-order dependence structures of graphs. We introduce multivariate complexity and dependence measures for graphs within the framework o ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

The European Union's Green Deal aims for a 55% reduction in greenhouse gas emissions by 2030. To reach this goal, a massive integration of Renewable Energy Sources (RES) into the power grid is necessary. As RES become a large part of the electricity genera ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

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 ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Chattez avec Graph Search

Multimodal Prediction Framework for Total Shoulder Arthroplasty Outcomes: Integrating Morphological, Musculoskeletal, Finite Element, and Bayesian Causal Models

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Predicting the long-term collective behaviour of fish pairs with deep learning

Stress tolerance of lightweight glass-free PV modules for vehicle integration

Topics in statistical physics of high-dimensional machine learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

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

Incorporating Projective Geometry into Deep Learning

An Information-Theoretic Framework for Multiplex Graphs

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

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Advanced control strategies to exploit the hydropower potential enhancing ancillary services provision to the power system

Unveiling the complexity of learning and decision-making

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Network time series forecasting in photovoltaics power production

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Understanding generalization and robustness in modern deep learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning