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.
Results
Showing 1 to 20 of 165 for “"generative model"”.
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Software Library for Generative Model Applications
The generation of data by machine learning models is a powerful concept that has impacted the field of Artificial Intelligence in the past few years. In this thesis, we focus on building a software library to facilitate the workflow, evaluation, and analysis of generative models. Our work is …
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A generative model for activations in functional MRI
… functional properties of the brain. We propose a generative model that jointly explains neural activation and temporal activity in an fMRI experiment. We derive an algorithm for inferring activation patterns and estimating the temporal response from fMRI data, and present results on synthetic and …
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Learning Disentangled Representations of Holograms via Deep Generative Model
… It demonstrates the strength of learning models and also identifies the challenges and limitations of conventional hologram computation methods for 3D scenes. These motivate us to explore the issues and seek solutions via deep generative models. We propose an original concept of …
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Robustness and Adaptation via a Generative Model of Policies in Reinforcement Learning
… an external reward. To this end, we introduce a generative model of policies which maps a low-dimensional latent space to an agent policy space. In order to learn a broad range of solutions, our generative model uses a diversity regularizer that incentivizes different agent behaviors given the …
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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 this …
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Synthesizing a complete tomographic study with nodules from multiple radiograph views via deep generative models
… 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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Inferring Shape and Material from Sound
… we iterative update current estimates given a generative model. This, however, requires sophisticated generative models, which is too computationally expensive to support iterative inference. Finally, despite the popularity of deep learning methods in auditory perception tasks, most of them are …
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Distribution distance measures in generative and privacy models
… measures provide a useful class of tools for generative and privacy models. In both cases, the goal is to simulate a data distribution without revealing too much about individual points. While early generative models focused on matching data in a component-wise manner, the models in this work …
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Inferring final plans : expanding on a generative and logic-based approach
… design or execution. Previous work shows that a generative model with logic-based priors is effective when the plan being formed is relatively simple. We present an algorithm that expands on the model by incorporating dialogue acts, which give an indication of how proposed actions are said. We …
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Detecting Brain Effective Connectivity with Supervised and Bayesian Methods
… that is based on the multivariate autoregressive model, where we face the problem of model identification. For this purpose, we present a new Bayesian method for linear model identification and we explore its capability of modeling the sparsity structure of the signals. As a second contribution, …
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Inference of electromagnetic system behavior in the presence of variability
… by applying novel statistical inference and generative modeling techniques to the extraction of statistical models of system responses. Machine learning based and physics based techniques are introduced to address the limitations of existing methods by incorporating dimensionality reduction …
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Application of generative models in speech processing tasks
Generative probabilistic and neural models of the speech signal are shown to be effective in speech synthesis and speech enhancement, where generating natural and clean speech is the goal. This thesis develops two probabilistic signal processing algorithms based on the source-filter model of speech …
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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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Capsule Networks: Framework and Application to Disentanglement for Generative Models
Generative models are one of the most prominent components of unsupervised learning models that have a plethora of applications in various domains such as image-to-image translation, video prediction, and generating synthetic data where accessing real data is expensive, unethical, or compromising …
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Learning Models for Multi-Viewpoint Object Detection
We also propose two different approaches for modeling the inter-part relations and algorithms for efficiently learning the model parameters. The first approach uses a generative model that models the joint probability distribution over the locations and visibility of all the object parts. The …
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PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming
… naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered, corrupted, and joined to yield incomplete, dirty, and denormalized datasets. Based on this …
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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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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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Cooperate to compete : composable planning and inference in multi-agent reinforcement learning
… of their behavior. We describe a formal generative model that composes individual planning programs into rich and variable teams. This model constructs optimal coordinated team plans and uses these plans as part of a Bayesian inference of collaborators and adversaries of varying …
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Learning Generative Models Using Structured Latent Variables
… example for a new concept is available to the model for training. It is widely believed that learned prior knowledge must be utilized in order to tackle this problem. My dissertation tries to address some of these concerns by introducing domain-specific knowledge to standard deep learning …
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