Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 23 for “"Geometric Deep Learning"”.
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Geometric Deep Learning for Biomolecules
Recent advancements in machine learning offer a promising pathway to deeper insights into biological phenomena. This manuscript explores the integration of geometric deep learning techniques to model biological structures. By embedding inductive biases based on geometry and physical laws, we aim to …
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Geometric Deep Learning for Healthcare Applications
… of Graph Neural Networks (GNNs), a subset of Geometric Deep Learning methods, for medical image analysis and causal structure learning. Tracking the progression of pathologies in chest radiography poses several challenges in anatomical motion estimation and image registration as this task …
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Learning NP-hard problems on networks using Geometric Deep Learning
… sub-optimal heuristics. In this work we show how Geometric Deep Learning, the generalization of Deep Learning to non-Euclidean domains like graphs, can aid the computation of NP-hard problems and learn heuristics from the data. Specifically, we define a framework, namely GDM, to learn how to solve …
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Shape and Function in Coronary Artery Disease Deciphered through Geometric Deep Learning and Statistical Shape Modelling
L'abstract è presente nell'allegato / the abstract is in the attachment
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Geometrically and Perceptually Accurate Facial Mesh Synthesis and Personalised Blendshapes Generation using Graph Neural Networks
The importance of geometric deep learning applications to 3D content creation has in- creased rapidly, driven by significant investments in the next generation Virtual Reality platforms and Visual Effects intensive productions. Generation of high fidelity digital humans became a focal point and one …
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Prospects for Quantum Equivariant Neural Networks
… computers to realize these structures in machine learning. This work reviews the mathematical machinery necessary from group representation theory, surveys the theory of equivariance, and combines results in non-commutative harmonic analysis and geometric deep learning. Convolutions and …
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Collective behavior over social networks with data-driven and machine learning models
… I develop mathematical modeling (e.g., machine learning, game theory, and network science) and large-scale behavioral data to study collective behaviors over social networks. My dissertation will tackle this area in four directions, revolving around the intricate linkage between individuals' …
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Enhancing surrogate models of engineering structures with graph-based and physics-informed learning
… this work leverages recent advancements in geometric deep learning to propose a graph-based surrogate model (GSM). The GSM learns directly on the geometry of a structure and thus can learn on designs from multiple sources without the typical restrictions of a parametric design space. …
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Hidden Influence in Dynamic Networks
… I begin with a foundational overview of graph learning techniques and the specific models utilized in my work. The body of this dissertation is divided into three core sections. The first examines the orchestration of influence campaigns by state-backed entities on social media, utilizing the …
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Learning from Structured Data with Weak Supervision
… over the past decade include self-supervised learning methods that train models on broad data at scale without pre-defined labels, geometric deep learning that leverages structure and geometry informed by scientific knowledge, and generative AI methods that create action plans for experiments …
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Context-aware pedestrian intent prediction for connected and automated vehicles
… shift towards a new trend in extending deep neural networks into non-Euclidean spaces, commonly known as geometric deep learning. Research in deep learning on graphs is gaining momentum, showcasing the powerful descriptive capabilities of graph structures. These structures provide …
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Determination of the CKM ratio $|V_{ub}|/|V_{cb}|$ using semileptonic $B_c^{+}$ decays at LHCb
… this thesis details the development of a novel deep learning architecture to perform calorimetric shower reconstruction at high energy particle physics experiments. A bespoke network, exploiting recent developments in image recognition and geometric deep learning, is designed to achieve one-shot …
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Few Shot Learning for Rare Disease Diagnosis
… for rare disease patients. Recent advances in deep learning have considerably improved the accuracy of medical diagnosis. However, much of the success thus far is contingent on the availability of large annotated datasets containing thousands of examples per condition for training machine …
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Attention-based representation learning on graphs
… readily available, the field of representation learning has continued to evolve through approaches that seek to describe, understand, and even unify deep learning strategies for data structures such as sets, grids, and graphs. A remarkably successful application of this field of geometric deep …
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The Nucleosome Remodelling & Deacetylase complex: Genome folding & transcriptional regulation
Genome function is highly dependent on the three dimensional organisation of chromatin within a cell’s nucleus. Approximately 2 metres of DNA is folded to fit within a nucleus which is on the order of a few micrometers. In addition to simply fitting within the space available, this folding directly …
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BRIDGING INTERPRETABILITY AND PERFORMANCE IN 3D DEEP LEARNING THROUGH GEOMETRIC INDUCTIVE BIASES
Questa tesi indaga come i geometric inductive biases possano risolvere limitazioni fondamentali nel deep learning 3D, in particolare per applicazioni critiche di sicurezza come l’ispezione delle reti elettriche. Nonostante i progressi significativi nel campo del 3D scene understanding, la maggior …
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Machine Learning with Geometric Algebra: Multivectors for Modelling, Understanding and Computing
Geometric Algebra (GA) has been successfully applied in several fields, including physics, graphics, and robotics, but its potential in Machine Learning (ML) and Deep Learning (DL) remains largely unexplored. This thesis addresses that gap by investigating the application of GA to a variety of ML …
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Deep Learning for Brain Structural Connectivity Analysis: From Tissue Segmentation to Tractogram Alignment
… on priors based on normal anatomy. Recently, deep learning (DL) has shown the potential of supervised data-driven approaches for brain tissue segmentation by leveraging the information encoded in the signal intensity of T1-w images. As a first contribution of this thesis, we reported empirical …
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Group theory and information theory algorithms in deep learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Biomolecular Simulations with Machine Learning Potentials
… with a purely data driven approach and learning to reproduce the outputs of more expensive electronic structure calculations at greatly reduced computational cost. This thesis introduces new developments that enable modelling (bio)molecular systems with machine learned forcefields. …
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