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.
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Showing 1 to 8 of 8 for “"Conditional Generation"”.
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Designing Novel DNA-Binding Proteins with Generative Deep Learning
… protein struc- tures and diffusion models for conditional sampling. The GNNs capture the intricate relationships between amino acids in the protein backbone, allowing for the effective encoding of structural information relevant to DNA binding. The diffusion models enable the conditional …
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Latent Walking Techniques for Conditioning GAN-Generated Music
Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Generating music is very difficult; components like long and short term structure present time complexity, which can be difficult for neural …
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Towards a Unified Framework for Visual Recognition and Generation via Masked Generative Modeling
Recognition and generation are two key tasks in computer vision. However, recognition and generative models are typically trained independently, which ignores the complementary nature of the two tasks. In this thesis, we present a unified framework for visual data recognition and generation via …
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Guiding diffusion generative models with applications to inverse problems
Conditional sampling via denoising diffusion models (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 …
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Recurrent Neural Network Language Generation for Dialogue Systems
… information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses …
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Topics in Deep Generative Modelling Mathematical and Computational Aspects of Diffusion Models and Generative Adversarial Networks
… and novel algorithms, addressing areas like conditional generation, dimensionality estimation, and reduction. Firstly, we examine diffusion models from a mean-field perspective, which leads to a new theoretical insight into the differences between stochastic and deterministic sampling schemes …
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Methods for Constructing and Exploiting Information Measures for Neural Networks
… we progress to the topic of diffusion and conditional generation and present our paper on importance-guided diffusion. Diffusion models are a class of generative models inspired by non-equilibrium thermodynamics which have become extremely popular in recent years for image synthesis and …
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Generative Adversarial Networks for Inverse Design Problems in Engineering: Methods to handle performance, constraints, and creativity requirements
… performance requirements by 69\% in an airfoil generation task and up to 78\% in synthetic conditional generation tasks and achieves greater design space coverage. The proposed method enables efficient design synthesis and design space exploration, however, the problem of handling constraints …