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 152 for “"Diffusion models"”.
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Reaction-Diffusion Models of Cancer Dispersion
… and temporal evolution of one-dimensional models of cancer dispersion are studied. The models discussed pertain primarily to the transition of a tumor from an initial neoplasm to the dormant avascular state, i.e. just prior to the vascular state, whenever that may occur. Initiating the …
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Essays on continuous time diffusion models.
During the past few decades, continuous time diffusion models have become an integral part of financial economics. Especially, in certain core areas in finance, such as interest rate, asset pricing, option pricing, portfolio selection and volatility modelling, continuous time diffusion models have …
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Text recaptioning for audio diffusion models
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Unsupervised Compositional Image Decompositionwith Diffusion Models
… of the world. This paper introduces Decomp Diffusion, an unsupervised method for decomposing images into a set of underlying compositional factors, each represented by a different diffusion model. We demonstrate how each decomposed diffusion model captures a different factor of the scene, …
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Extending reinforcement learning techniques for diffusion models
El Aprendizaje por Refuerzo (RL) se ha convertido en una herramienta crucial para alinear modelos generativos complejos, superando las limitaciones de los métodos de aprendizaje supervisado tradicionales. Su capacidad para optimizar recompensas arbitrarias, incluyendo funciones escalares no …
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Controlled training data generation with diffusion models
In this work, we present a method to control a text-to-image generative model to produce training data specifically “useful” for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a language model or human expertise, …
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Can diffusion models capture extreme event statistics?
… has been increasing in the usage of generative models trained on available data. Despite the sporadic success of such methods, it is not clear for what systems or datasets a system-agnostic generative AI tool is capable of generating previously ‘unseen’ extreme events in a manner that accurately …
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Composing Visual Relations with Composable Diffusion Models
… relations. Recent work in text-guided diffusion models have produced impressive results in generating photorealistic images, but such models often fail to capture spatial relationships between objects, and will often generate scenes where individual specified relations are incorrectly …
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Modeling Molecular Structures with Intrinsic Diffusion Models
… molecular system. This thesis proposes Intrinsic Diffusion Modeling, a novel approach to this problem based on combining diffusion generative models with scientific knowledge about the flexibility of biological complexes. The knowledge of these degrees of freedom is translated into the definition …
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Factorization and Compositional Generalization in Diffusion Models
… connection to compositionality, remain unclear. Diffusion models are capable of generating photorealistic images that combine elements not co-occurring in the training set, demonstrating their ability to compositionally generalize. Yet, the underlying mechanisms of such compositionality and its …
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On the Inductive Biases of Conditional Diffusion Models
Diffusion models have achieved remarkable progress in recent years across various domains and applications, but how diffusion models generalize is still not well understood. While prior work predominantly focuses on unconditional diffusion models, in this thesis we focus on understanding …
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Generative Diffusion Models Towards De Novo Protein Design
… This thesis aims to develop deep learning models that generate novel structures and sequences that can be guided towards desired functions. We first describe novel generative models that learn to generate protein structures and sequences by developing diffusion models over general state …
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Renewal process and diffusion models of 1/f noise
Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979.
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Numerical Analysis of Jump-Diffusion Models for Option Pricing
Jump-diffusion models can under certain assumptions be expressed as partial integro-differential equations (PIDE). Such a PIDE typically involves a convection term and a nonlocal integral like for the here considered models of Merton and Kou. We transform the PIDE to eliminate the convection term, …
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Reconstructing Visual Appearance and Process by Repurposing Pretrained Diffusion Models
… processes that produced the final result, yet models are expected to synthesize outputs that are visually complete, coherent, and plausible. This setting places strong demands on generative models, requiring them to infer missing information, integrate fragmented evidence, and reconstruct …
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Analysis of diffusion models in high speed reacting flow simulations
… of this study was to analyze the effect of mass diffusion in a high speed reacting flow field. Diffusion processes have typically been neglected in high speed reacting flows as the time scales associated with convective and reactive processes are found to be much shorter than diffusive time …
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Case analysis studies of diffusion models on E-commerce transaction data
… there are numerous software and statistical models used in online retail, not many practitioners claim success creating accurate online inventory management or marketing effectiveness forecast models. Thus, online retailers with both online and offline strategies express frustration that …
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Glass onion: Compositional text-to-image generation using diffusion models and LLMs
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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Towards Learning the Geometry of Data: From Diffusion Models to Riemannian Geometry
… scales. In chapter 4, we introduce non-uniform diffusion models, which apply faster diffusion speed to high-frequency information and slower diffusion speed to lower-frequency information, creating a multi-scale structure similar to multi-scale normalising flows. This approach outperforms …
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Applications of Field Theory to Reaction Diffusion Models and Driven Diffusive Systems
… density. Two of the fit parameters, namely the diffusion constant and the noise strength, deviate from their naive bare values [6]. In particular, the former increases significantly with the system size. Since this behavior can only be caused by nonlinear effects, we calculate the lowest order …
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