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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 76 for “"variational inference"”.
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Variational Inference in Dynamical Systems
… in our data. The framework of statistical inference gives us the tools to do so, yet, for many systems of interest, performing inference exactly is not computationally or analytically tractable. The contribution of this thesis, then, is twofold: first, we uncover two sources of bias in …
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Variational inference for non-stationary distributions
In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as …
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Towards Improved Variational Inference for Deep Bayesian Models
… combined with a likelihood to perform posterior inference. Unfortunately, for deep models, the true posterior is intractable, forcing the user to resort to approximations. In this thesis, we explore the use of variational inference as an approximation, as it is unique in simultaneously …
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Distributed inference : combining variational inference with distributed computing
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using …
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CARE: certifiably robust learning with reasoning via variational inference
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Improved Sampling and Variational Inference Methods for Neural Networks
… Networks (BNNs), an application of Bayesian inference to neural networks, offer an alternative way of training. They combine multiple weight settings, each compatible with the training data, and quantify the uncertainty about the network's weights. While Bayesian inference applied to neural …
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Cutting plane algorithms for variational inference in graphical models
… upper bound on the entropy, this gives a new variational inference algorithm for probabilistic inference in discrete Markov Random Fields (MRFs). Valid constraints are derived for the marginal polytope through a series of projections onto the cut polytope. Projecting onto a larger model gives …
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Variational Inference Using Approximate Likelihood Under the Coalescent With Recombination
… introduce in this thesis a novel method, VICAR (Variational Inference under the CoAlescent with Recombination), for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… solving various problems relating to PDEs though variational inference and probabilistic models. The work is composed of three contributions. The first contribution lies in creating active learning surrogates for Bayesian inverse problems called Active learning projected surrogates – SVGD. Here we …
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Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
… uncertainty estimation capabilities of Bayesian Inference, offering a robust framework to address challenges such as overconfidence and overfitting. However, the inherent complexity of BNNs —due to the use of weight distributions— renders the process computationally intensive, thereby hindering …
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Provable Algorithms for Learning and Variational Inference in Undirected Graphical Models
… from samples, and related problems of performing inference on a known model, both areas of research which have been the subject of continued interest over the years. Our main contributions are the first computationally efficient algorithms for provably (1) learning a (possibly ill-conditioned) …
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
… require an infinite number of parameters, inference algorithms are needed to make posterior calculations tractable. The focus of this work is an evaluation of three of these Bayesian variational inference algorithms which have only recently become computationally viable: Accelerated …
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Unsupervised learning of disentangled representations for speech with neural variational inference models
… data. We start with investigating an existing variational autoencoder (VAE) model for learning latent representations, and derive novel latent space operations for speech transformation. The transformation method is applied to unsupervised domain adaptation problems, which addresses the …
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Autoencoding variational inference for the visualization of velocity-enriched scRNA-seq data
… information. In this work, we leverage variational inference to design deep learning models that use expression data and velocity data in tandem to produce effective low-dimensional representations. We also provide a methodology for RNA-seq data imputation using the learned models, …
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Differential Games of Mixed Strategies in a Variational Inference Framework: An Application to the Perimeter Defense Problem
… game of mixed strategies as an adversarial Variational Inference (VI) problem so that it can be solved through the lenses of inference. To achieve the aforementioned goal, the dissertation extends two existing tools for solving non-adversarial VI problems to the adver- sarial setting where …
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Advances in Compression using Probabilistic Models
… a probabilistic model for the data is defined, variational inference can be used to infer its parameters from data. Variational inference is closely related to the optimal compression size, as stated by Hinton's bits-back argument: the evidence lower bound, the objective optimized by variational …
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Deep Generative Models and Biological Applications
… distributions. </p><p>The recent proposed Variational auto-encoders (VAE) framework is an efficient high-dimensional inference method to modeling complicated data manifold in an approximate Bayesian way, i.e., variational inference. </p><p>We first discuss how to design fast stochastic …
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Proximal Gradient Algorithms for Gaussian Variational Inference:Optimization in the Bures–Wasserstein Space
Variational inference (VI) seeks to approximate a target distribution π by an element of a tractable family of distributions. Of key interest in statistics and machine learning is Gaussian VI, which approximates π by minimizing the Kullback–Leibler (KL) divergence to π over the space of Gaussians. …
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Scalable Gaussian process inference using variational methods
… is non-Gaussian. In this thesis, we study variational inference as a framework for meeting these challenges. An introductory chapter motivates the use of stochastic processes as priors, with a particular focus on Gaussian process modelling. A section on variational inference reviews the …
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Probabilistic Machine Learning for Circular Statistics: Models and inference using the Multivariate Generalised von Mises distribution
… statistics. On the other hand, the approximate inference frameworks from probabilistic machine learning have only recently started to the circular statistics landscape. This thesis intends to redress the gap between these two fields by contributing to both fields with models and approximate …
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