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 47 for “"Approximate Inference"”.
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Approximate Inference: New Visions
… Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision making, and …
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Approximate Inference in Variational Autoencoders
… in order to train this model, we must perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent variable model. This thesis provides novel analyses, applications, and interpretations of …
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Approximate inference in graphical models
… of complex hierarchical models for real-world inference tasks. Unfortunately, exact inference in probabilistic models is often computationally expensive or even intractable. A close inspection in such situations often reveals that computational bottlenecks are confined to certain aspects of the …
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Approximate inference in Gaussian graphical models
The focus of this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to describe complex …
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Approximate inference methods for grid-structured MRFs
… and graph cuts methods for performing approximate inference on an MRF. I developed a method by which the memory requirements for belief propagation could be significantly reduced. I also developed a modification of the graph cuts algorithm that allows it to work on MRFs with very …
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Approximate inference in graphical models using LP relaxations
… to computational biology. Exact probabilistic inference is generally intractable in complex models having many dependencies between the variables. We present new approaches to approximate inference based on linear programming (LP) relaxations. Our algorithms optimize over the cycle relaxation …
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Approximate inference : decomposition methods with applications to networks
… constraints. We propose a polynomial time approximate algorithm to determine whether a, given vector of end-to-end rates between various source-destination pairs can be supported by the network through a combination of routing and scheduling decisions. Lastly, we investigate the problem of …
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Scalable Approximate Inference and Model Selection in Gaussian Process Regression
… one of only a handful of Bayesian models where inference can be performed without the need for approximation. However, a frequent criticism of these models from practitioners of Bayesian machine learning is that they are challenging to scale to large datasets due to the need to compute a large …
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Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
… research since the 1990s. They rely on Bayesian inference to represent uncertainty in the weights of a neural network. On the other hand, neural processes are a recently introduced model that relies on meta-learning rather than Bayesian inference to obtain uncertainty estimates. This thesis …
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Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy
… be able to reason under uncertainty. Bayesian inference, powered by the probabilistic framework, is believed to be a principled way to incorporate uncertainty into the decision making process. The difficulty of applying Bayesian inference in practice is rooted in the intractability of computing …
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Structure in Machine Learning: Graphical Models and Monte Carlo Methods
This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong …
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Probabilistic Machine Learning for Circular Statistics: Models and inference using the Multivariate Generalised von Mises distribution
… by circular 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 …
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Advances in Bayesian Machine Learning: From Uncertainty to Decision Making
… many machine learning applications. To this end, approximate inference algorithms are developed to perform inference at a relatively low cost. Despite the recent advancements of scaling approximate inference to “big model $\times$ big data” regimes, many open challenges remain. For instance, how …
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Advances in Software and Spatio-Temporal Modelling with Gaussian Processes
… to a design for software, including exact and approximate inference algorithms. I demonstrate the utility of this software through a collection of worked examples, focussing on models which are more cleanly and easily expressed using this new software. Secondly, I develop a new scalable …
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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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A novel inference algorithm on graphical model
We present a framework for approximate inference that, given a factor graph and a subset of its variables, produces an approximate marginal distribution over these variables with bounds. The factors of the factor graph are abstracted as as piecewise polynomial functions with lower and upper bounds, …
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Approximating the Log-Partition Function
… from a large number of NP-hard problems to inference tasks such as computing the partition function (exact inference) or approximating the log-partition function (approximate inference). In this master thesis, we will motivate the need for a general constant-factor approximations of the …
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On the multivariate components of variance problem
<p>Statistical procedures for making inferences on the variance components in univariate mixed effect models have been developed and extensively used in many fields. Development for multivariate mixed models has been relatively limited. One important issue in the multivariate problem is determining …
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Scaling Bayesian inference : theoretical foundations and practical methods
Bayesian statistical modeling and inference allow scientists, engineers, and companies to learn from data while incorporating prior knowledge, sharing power across experiments via hierarchical models, quantifying their uncertainty about what they have learned, and making predictions about an …
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Exploration vs. exploitation in coupon personalization
… in the long run. In this thesis we 1) derive approximate inference algorithms to learn customer preferences from purchase data in real time, 2) formulate the retailers' offer allocation problem as a multi armed bandit and explore solution strategies.
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