With the onset of artificial intelligence (AI), machine learning (ML) approaches have become increasingly embedded into the drug discovery pipeline with the aim of accelerating research and reducing cost. Exemplary applications include quantitative structure-p…
Data visualization is essential for navigating the vast volume of data that defines our time, letting humans use perception to make sense of complex datasets. Yet creating visualizations requires mastery of specialized programming languages, producing a gap be…
Recent advances in visual and multimodal learning have demonstrated impressive performance on diverse tasks. However, their reliability in real-world scenarios remains challenged by issues of robustness, adaptation, and generalization. In this thesis, we tackl…
Autonomous driving has progressed rapidly in recent years, driven largely by advances in machine learning, computer vision, and probabilistic optimization methods. Despite this progress, the deployment of autonomous systems in complex, safety-critical real- wo…
Medical Image Semantic Segmentation (MISS), the process of assigning a semantic label to each pixel in an image, is a foundational task in computational medicine, critical for quantitative diagnostics and treatment planning. However, developing robust MISS mod…
Recent advancements in Computer Vision (CV) and Machine Learning (ML) have fundamentally revolutionized the field of human behaviour understanding, enabling intelligent systems to move beyond simple motion detection to the interpretation of complex, semantic a…
This thesis evaluates self-supervised, semi-supervised, supervised, and rule-based machine learning approaches for anomaly detection in low-frequency LoRaWAN refrigeration monitoring across 84 sensors.
Self-supervised learning (SSL) has shown promise for extracting useful representations from unlabelled tabular data, but recent work has found that SSL embeddings do not improve unsupervised anomaly detection. This thesis investigates whether these limitations…
Embodied AI introduces a new generation of intelligent agents capable of closed-loop perception and control in real-world environments. However, despite recent progress in robot learning, Most current approaches to robot manipulation are heavily based on super…
Most traditional classification methods process each data point in isolation, ignoring the structural information embedded in the relationships among data points. This dissertation develops the contrastive attention transformer, a novel classification architec…
s systèmes d’apprentissage automatique (AA), y compris les modèles classiques, les réseaux de neurones profonds (DNN) et les grands modèles de langage (LLMs), sont de plus en plus déployés dans des domaines à enjeux élevés tels que la santé, le recrutement, la…
This study examines whether self-supervised learning (SSL) improves anomaly detection performance in network intrusion detection systems when labeled data is limited. Although machine learning—based systems have advanced intrusion detection, their effectivenes…
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قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
