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 22 for “"Generative modelling."”.
-
Generative modelling and adversarial learning
… of real-world data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for deriving generative models from complicated real-world data. GAN and its variants use a generator to synthesise …
-
Generative modelling under epistemic uncertainty
… contribution of this work lies in re-imagining Generative AI under epistemic uncertainty. We introduce Random-Set Large Language Models (RS-LLMs), which predict belief functions over token sets to quantify second-order uncertainty, thereby providing a robust mechanism for hallucination …
-
Using generative modelling in healthcare
… We shall also present two popular classes of generative models, namely Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) and their variants. Subsequently, we shall review some new developed imputation methods which are based on GANs and VAEs. We shall assess their …
-
Topics in Deep Generative Modelling Mathematical and Computational Aspects of Diffusion Models and Generative Adversarial Networks
… the theoretical and practical aspects of deep generative models with a special emphasis on score-based diffusion models. It also includes studies of theoretical underpinnings of generative adversarial networks and introduces novel algorithmic improvements to variational autoencoders. The work …
-
Generative representations of 2D and 3D visual content: semantics, geometry, and appearance
Generative modelling has transformed how visual data is represented, synthesized, and manipulated across computer vision and graphics. From images and 3D shapes to material appearance, generative models offer the potential to create content that is diverse, controllable, and physically realistic. …
-
Relaxing assumptions in deep probabilistic modelling
… Given this success, deep learning and deep generative modelling have progressively been applied across a broader range of increasingly demanding applications, as well as in safety-critical domains such as healthcare. However, existing models are reliant upon restrictive theoretical …
-
Quantum Algorithms for Solving Differential Equations with Application to Computational Fluid Dynamics
… preconditioning, variational hybrid strategies, generative modelling, quantum data processing and physics-informed quantum machine learning. These methods are demonstrated on representative CFD use cases, capturing essential flow features while accounting for quantum resource constraints. The …
-
The resurgence of structure in deep neural networks
… language processing, reinforcement learning and generative modelling. These success stories nearly universally go hand-in-hand with availability of immense quantities of labelled training examples ("big data") exhibiting simple grid-like structure (e.g. text or images), exploitable through …
-
Probabilistic Modelling in Function Space
… computational scalability. In contrast, deep generative models, while not offering closed form solutions, excel at learning data-driven priors and inherently scale to large datasets using neural networks. This thesis aims to bridge the gap between the principled but computationally expensive …
-
Generative adversarial networks for fine art generation
Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing …
-
Towards Learning the Geometry of Data: From Diffusion Models to Riemannian Geometry
This thesis establishes novel connections between generative modelling, self-supervised learning, and Riemannian geometry, paving the way for learning the intrinsic geometry of data manifolds. In chapter 3, we introduce CAFLOW, a conditional normalising flow that improves image-to-image translation …
-
Generating Trustworthy Synthetic Data
… improving AI trustworthiness. Advances in deep generative modelling have made synthetic data more real than ever, and as a result there is a steep rise in research that aims to replace real data with synthetic data. The first uses of synthetic data were mostly privacy-focused, aiming to create …
-
Drug discovery for misfolding diseases using structure-based iterative learning
… pipelines. This issue is pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. The high attrition rate of neurodegenerative drug discovery is especially acute for Parkinson’s disease, where no disease-modifying drugs have …
-
On Disentangled Analysis-by-Synthesis Shape Representations
… perceptual inference as the inversion of a generative process. This provides several advantages: weak supervision, via the reconstructive signal, and the opportunity for disentanglement, via regularizing priors. The resulting representations are more versatile, controllable, and widely …
-
Guiding diffusion generative models with applications to inverse problems
… (DDMs) has received significant interest in generative modelling for their scalability, improved sample quality, and versatile application. These models are widely used in scientific and industrial settings, where they leverage latent representations of data and complex relationships that …
-
Neural Network Training and Inversion with a Bregman Learning Framework
… driven due to the rapid advancements in generative modelling in recent years. While several approaches for the inversion of DNNs have been proposed, the stability of the inversion is an often neglected crucial aspect. The neural network inversion problem is ill-posed as the solution does …
-
Encoding parameter and structural efficiency in deep learning
… language processing, reinforcement learning, generative modelling and more recently relational learning from graph-structured data. The main reason for this success is an increase in the availability of computational power, which allows for deep and highly parameterized neural network …
-
Approximate Inference in Variational Autoencoders
… latent variable model is a powerful tool for modelling complex distributions. However, in order to train this model, we must perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent …
-
Towards Data Efficiency and Controllable Representations for Deep Learning in Resource-Constrained Domains
… better than random sampling. For data synthesis, generative models face their own set of challenges. Despite their promise for synthetic data generation, these models frequently lack mechanisms to disentangle generative factors at the representation level, limiting their controllability. …
-
Statistical Inference and Learning for Stochastic and Partial Differential Equations
… of change, derived from physical laws and modelling assumptions. Practically, however, mechanistic descriptions alone may prove insufficient to accurately model observed data; the model misspecification can be due to over-simplification, inaccurately specified parameters, or missing …
Page 1 of 2