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 43 for “"Deep Generative Models"”.
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Deep Generative Models and Biological Applications
… in a wide variety of applications. </p><p>Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability distributions. </p><p>The recent proposed Variational …
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Deep generative models for speech editing
Generative models are very useful for generating and modifying natural-sounding speech in various speech processing tasks such as speech synthesis, speech enhancement, and voice conversion. There are two ways that the generative models can help in naturalness for speech processing. The first way is …
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Deep generative models via explicit Wasserstein minimization
This thesis provides a procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The approach is based on two principles: (a) if the source randomness of the network is a continuous distribution (the …
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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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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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Learning meaning representations for text generation with deep generative models
… an auxiliary variable decomposition. All of the models that we use combine a high-level graphical model with a neural language model text generator. The graphical model lets us specify the structure of the text generating process, while the neural text generator can learn how to generate fluent …
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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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Advance metabolite identification from tandem mass spectra using deep generative models
… of metabolites and spectra. Further, we build a generative adversarial network (GAN) to optimize the classifier as the discriminator. A large number of experiments are conducted. The experimental results verify the effectiveness of our tool.
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Missing data imputation in a clinical registry with deep generative models
… model, or cause failures in the deployment of models that require a complete input. A clinical registry is a record of patients information about their health history, status and the healthcare they receive during various periods of time. Due to the challenge of data collection and the …
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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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Deep Generative Models for Unsupervised Scale-Based and Position-Based Disentanglement of Concepts from Face Images.
… recent advances of artificial intelligence using deep neural networks, computers are still struggling at achieving a rich and flexible understanding of face images comparable to humans' face perception abilities. This thesis aims at finding fully unsupervised ways for learning a transformation …
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Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
… of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large …
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Fine-tuning generative models
Deep generative models have emerged as a powerful modeling paradigm for making sense of large amounts of unlabeled real-world data. In particular, the representations produced by these models have proven to be useful both in improving human understanding of the factors of variation in the original …
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Improving Deep Learning with Probabilistic Approaches
… its successes in scaling to real-world problems, deep learning is not without flaws. In particular, it struggles with uncertainty quantification and data efficiency. Probabilistic methods, while currently somewhat underappreciated by the wider machine learning community, provide calibrated …
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Geometric representation learning for chemical property prediction, structure elucidation, and molecular design
… (potentially labeled) molecular structures via deep neural networks. In predictive chemistry, deep learning is increasingly being used to replace expensive physics-based simulations and even experimental measurements of chemical properties. In generative chemistry, deep generative models are …
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Representation learning with random images
… we investigate a suite of image generation models that produce images from simple random processes. These are then used as training data for a visual representation learner with a contrastive loss. We study two types of noise processes, statistical image models and deep generative models …
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Towards perceptual metrics for audio quality assessment
… considerable advances in audio synthesis with deep generative models known as Neural Audio Synthesizers (NAS). However, the state-of-the-art is very difficult to quantify directly; different studies use different evaluation methodologies and metrics when reporting results, making a direct …
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Rethinking Maximum Likelihood Estimation
… parameter estimation, with a particular focus on deep generative models. I propose a novel alternative to Maximum Likelihood Estimation, most popular method for estimating parameters, and show how my proposed method mitigates bias, reduces overfitting and the overrepresentation of high frequency …
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Deep Representation Learning on Labeled Graphs
… challenging for ICA. As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks …
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