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 19 of 19 for “"Deep Generative Model"”.
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Learning Disentangled Representations of Holograms via Deep Generative Model
This thesis exploits deep learning to develop real-time, super-resolution hologram generation. It first gives an overview of both the technologies of deep learning and holographic 3D displays. It demonstrates the strength of learning models and also identifies the challenges and limitations of …
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Towards Uncovering the True Use of Unlabeled Data in Machine Learning
… data, (ii) whether is possible to scale existing models for positive unlabeled learning, and (iii) whether is possible to train a deep generative model with a single minimization problem.
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Synthesizing a complete tomographic study with nodules from multiple radiograph views via deep generative models
… density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study …
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Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods
… by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our …
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Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting
… we introduce TrajLearn, a Transformer‑based deep generative model that treats trajectories as token sequences and employs spatially constrained beam search to predict each individuals’s next k locations with high precision. Building on these forecasts, we present MobiNetForecast, which …
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Data-Driven Bicycle Design using Performance-Aware Deep Generative Models
This treatise explores the application of Deep Generative Machine Learning Models to bicycle design and optimization. Deep Generative Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. This work addresses several …
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Learning distributions with Particle Mirror Descent
… By marrying Bayesian probabilistic inference and deep neural networks, deep generative networks have shown remarkable success in various kinds of generative tasks. However, such models usually make an assumption that posterior distribution can be simply characterized as a Gaussian distribution, …
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Machine Learning for Reconstructing Dynamic Protein Structures from Cryo-EM Images
… images. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of cryo-EM volumes and a learning algorithm to optimize this representation from unlabeled 2D cryo-EM images. Released as an open source software tool, cryoDRGN has been applied on …
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Probabilistic Modelling in Function Space
… ensuring 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 …
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Learning from pre-pandemic data to design and test future-proof therapeutics
… that integrates fitness predictions from a deep generative model of evolutionary sequences with biophysical and structural information. EVEscape quantifies the immune escape potential of viral strains at scale and is applicable before surveillance sequencing, experimental scans, or 3D …
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Machine Learning for Structural Characterization and Generation: Applications to Small-Angle Scattering and Electron Microscopy
… novel uses for existing structures and for deep generative models to design new structures for a wide range of applications. This thesis is concerned with the development of machine learning (ML) algorithms for the characterization and generation of materials and nanostructures. Chapter 1 …
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Machine Learning Approaches for Equitable Healthcare
… and fairness of the resulting machine learning models. Because the observational data we collect can be noisy, incomplete, and biased, seemingly straight-forward implementation of existing methods for clinical intervention or better understanding human knowledge can lead to inaccurate and …
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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 …
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Statistical learning for cyber physical system
… the power of data-driven insights, predictive modeling, and advanced analytics, this research contributes to the development of smarter, safer, and more resilient transportation systems. Chapter 2 proposes a novel stochastic jump-based model to capture the driving dynamics of safety-critical …
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Knowledge discovery with recommenders for big data management in science and engineering communities
… that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) …
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Channel Estimation in TDD and FDD-Based Massive MIMO Systems
… algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).
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Generative Adversarial Networks for Inverse Design Problems in Engineering: Methods to handle performance, constraints, and creativity requirements
… we develop data-driven approaches based on the generative adversarial networks~(GANs) to address some of the main challenges in data-driven inverse design. First, we propose a new model, named Performance Conditioned Diverse Generative Adversarial Network (PcDGAN), which introduces a singular …
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Learning for Visual Synthesis and Transformation
… semantic annotations and images. Recently, deep generative learning has greatly promoted the development of visual synthesis. However, the existing generative methods still suffer from several issues, including model interpretation, controllability, stability, efficiency and performance. In …
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On Impact of Network Architecture for Deep Learning
… networks is a crucial factor in the success of deep learning models across a range of fields, including computer vision and natural language processing (NLP). Specific architectures are tailored to address particular tasks, and the selection of architecture can significantly affect the training …