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 131 for “"latent space"”.
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Latent Space Modeling for Environmental Systems
… thesis presents a comprehensive exploration of latent space modeling techniques developed to improve the accuracy and efficiency of environmental and hydrological predictions. The crux of this research lies in developing and applying machine learning models that can effectively integrate and …
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Code Similarity Search in a Latent Space
A huge database of program source codes that supports fast search via code similarity would be useful for several applications, including automated program synthesis and debugging, and user-facing code search in an integrated development environment. Here, "similar" is defined with respect to a set …
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Applied Machine Learning with Latent Space Representation and Manipulation
… and time-series signals prediction, etc. Latent space is a concept that is hidden but significant to machine learning, which helps extract features of data from different dimensions. In this dissertation, we try to apply machine learning with latent space representation and manipulation in …
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Leveraging the Latent Space for Model Understanding and Optimization
… interpretation. Second, models that leverage the latent space to represent data struggle to capture high-level details. This often results in reconstructions which do not accurately represent the original data. First, to address the issue of interpretability in text-to-image models, we introduce …
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Methods for Latent Space Interpretation via In-the-loop Fine-Tuning
… interaction with AI, the visualization of model latent space offers a novel modality of interpreting information. Embedding models have traditionally served as a means of retrieving relevant information to a topic by converting text into a high-dimensional vector. The high-dimensional vector …
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Text-Free Audio Captions of Short Videos from Latent Space Representation
In this thesis, we re-implement previous work exploring image to speech captioning. We expand upon the work to implement video to speech captioning. Specifically, we implement a text-free image to speech captioning pipeline that integrates four distinct machine learning models. We alter the models …
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Latent Space Dynamics and Security Implications in Machine Learning for Industrial Systems
L'abstract è presente nell'allegato / the abstract is in the attachment
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3D Face Reconstruction from a Front Image by Pose Extension in Latent Space
… enhancing the 3D face quality. Traditional image-space editing has limitations in manipulating content and styles while preserving high quality. However, editing in the latent space, which is the space after encoding or before decoding in a neural network, offers greater capabilities for …
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Computational and Statistical Detection of High-Dimensional Latent Space Structure in Random Networks
A probabilistic latent space graph PLSG (n, Ω, D, σ) is parametrized by its number of vertices n, a probability distribution D over some latent space Omega, and a connection function [mathematical function] such that [mathematical formula] almost surely with respect to D. To sample from …
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Neural implicit representations for engineering design
… diverse set of designs in a fixed length latent vector space. So, the goal of this thesis is to demonstrate the best implicit neural architecture for building latent space with design geometries that are diverse in their topologies and to demonstrate the methods in which the learned latent …
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Statistical methods for indirectly observed network data
… into existing surveys. This research develops a latent space model for ARD. This dissertation proposes statistical methods for methods for estimating social network and population characteristics using one type of social network data collected using standard surveys. First, a method to estimate …
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Real-Time Machine Learning for Quickest Detection
… decision process. Second, I discover the latent space characteristic of the zero-bias neural network and the method to mathematically convert a Deep Neural Network (DNN) classifier into a performance-assured binary abnormality detector. In this way, I can seamlessly integrate the deep …
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Robustness and Adaptation via a Generative Model of Policies in Reinforcement Learning
… 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 same state. Agents are assigned a specific latent vector …
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Inference Time Search for Protein Structure Prediction
… prediction models that give rise to a discrete latent space. We implement algorithms for searching and sampling in this discrete latent space and conduct experiments on a small model, demonstrating an increase in oracle and top-1-selected accuracy for predicted protein-protein complex structures.
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Anomaly Detection via Latent Variables Learned by Variational Autoencoders
… of the art modeling techniques that incorporate latent variables, hidden variables that are not directly observed but instead inferred from observed variables. Approaches to anomaly detection via variational autoencoders either adopt reconstruction error as a sole anomaly detection metric, …
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Three ploys for robust co-generation with generative adversarial nets
… by forward propagating a sample drawn from a latent space. However, an often overlooked shortcoming is their inability to find an arbitrary marginal distribution, which is useful for completion of missing data in tasks like super-resolution, image inpainting, etc., where we don’t know the …
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Neural network libor market model for pricing and hedging interest rate derivatives
… function (in the case of a one dimensional latent space) or the relationship of variables to copula input uniforms (in the case of a multi-dimensional latent space). The decoder will then train to learn the inverse of the encoder and this will then be used to generate data.
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Low dimensional visualization and modelling of data using distance-based models
… friendship) between nodes. This work is based on latent space models (LSM), which provide a generative model of network data: Nodes are placed in an auxiliary, low dimensional space, i.e., the latent space. Then, the distance between two nodes is used as a predictor for an edge, such that nodes …
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Learning distributions with Particle Mirror Descent
… estimate posterior distribution in primal space even with expectation constraint. 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 …
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