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 20 of 59 for “"Generative modeling"”.
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Generative Modeling with Guarantees
… focusing on improving the reliability of generative models while preserving their flexibility. First, we propose a framework that enables the generation of text conditionally using hard constraints, allowing users to specify certain elements in advance while leaving others open for the …
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Probabilistic generative modeling of speech
… speech synthesis also benefits from joint modeling. This thesis proposes a probabilistic generative model for speech called the Probabilistic Acoustic Tube (PAT). The highlights of the model are threefold. First, it is among the very first works to build a complete probabilistic model for …
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Generative modeling of sequential data
… thesis, we investigate various approaches for generative modeling, with a special emphasis on sequential data. Namely, we develop methodologies to deal with issues regarding representation (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), …
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Score Estimation for Generative Modeling
Recent advances in score-based (diffusion) generative models have achieved state-of-the-art sample quality across standard benchmarks. Building on the remarkable property of these models in estimating scores, this thesis presents three core contributions: 1) new objectives to reduce score …
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Generative modeling of dynamic visual scenes
Modeling visual scenes is one of the fundamental tasks of computer vision. Whereas tremendous efforts have been devoted to video analysis in past decades, most prior work focuses on specific tasks, leading to dedicated methods to solve them. This PhD thesis instead aims to derive a probabilistic …
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Generative modeling using the sliced Wasserstein distance
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to be hard to optimize and often unstable. While the aforementioned problems are particularly true for early GAN …
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Generative modeling of interactive and reactive digital humans
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Some advances in Bayesian inference and generative modeling
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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The Synthetic Data Vault : generative modeling for relational databases
… Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we developed an algorithm that computes statistics at the intersection of related database …
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Generative modeling of the tumor microenvironment : deconvolution, completion, and integration
… correspondences. This dissertation develops generative modeling methods for each of these three problems. We introduce BayesTME, a Bayesian framework for deconvolving aggregated spatial transcriptomics measurements without requiring paired single-cell references. BayesTME models spot-level …
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Theoretical Foundations of Flow-based Methods for Sampling and Generative Modeling
… map. This construction is applicable to both generative modeling and variational inference; when the map is invertible, one can also estimate the density of the target measure by evaluating the density of the pushforward of the source distribution under the inverse transport map. Over the past …
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Learning to draw vector graphics : applying generative modeling to font glyphs
… In this work, we explore the applications of generative modeling to the design of vectorized drawings, with a focus on font glyphs. We establish a data-driven approach for creating preliminary graphics upon which designers can iterate. To accomplish this, we present an end-to-end pipeline for …
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Developing frameworks for an equitable future: from building decarbonization to generative modeling.
… building decarbonization policy and generative modeling. Part 1 - Equitable building decarbonization Buildings significantly contribute to global carbon emissions, necessitating urgent decarbonization to meet 2050 climate targets. The U.S. strives for net-zero emissions by 2050, …
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New tools for Bayesian optimal experimental design and kernel-based generative modeling
… learning: optimal experimental design and generative modeling. Optimal experimental design (OED) is important to model development for science and engineering applications and beyond, especially when only a small number of observations can be taken or experiments performed, due to resource …
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Two approaches to robust hand pose estimation : generative modeling and semantic relations
… errors through a pipeline, we turn to generative modeling methods for hand pose estimation and present an inverse-graphics approach implemented in a probabilistic programming language. Spurred by the lack of occlusion in hand image datasets, we present the MIT Partially Occluded Hands …
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Towards a Unified Framework for Visual Recognition and Generation via Masked Generative Modeling
… 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 masked generative modeling, and further …
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Variational Autoencoders for Discovering Influential Latent Factors
Generative modeling is increasingly being used to simulate or generate new unseen data instances by means of modeling the statistical distribution of data. Generative modeling falls under the broad area of representation learning, which aims to discover representations required for detecting …
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Structured Diffusion Processes in Deep Generative Models
Diffusion generative models have emerged as a powerful, versatile, and elegant generative modeling framework for diverse data modalities. However, the high computational cost of inference relative to other frameworks remains a chief limitation of such models. At the same time, the design space of a …
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Machine Learning Approaches to Multi-Modal Data Integration and Translation in Single-Cell Biology
… In the first half, I develop methods based on generative modeling, representation learning and optimal transport to learn mappings between cells collected at different time points. In the second half, I develop methods based on generative modeling and representation learning to map between …
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