awesome-phd-thesis
I am presenting the PhD thesis that I have found useful and interesting
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I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.
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We discuss the application of Neural Spline Flows, a neural density
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It is well known in astronomy that propagating non-Gaussian prediction
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Making sense of a dataset in an automatic and unsupervised fashion is a
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Many real-life problems are represented as a black-box, i.e., the intern...
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We address the problem of building theoretical models that help elucidat...
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This paper provides a review of Approximate Bayesian Computation (ABC)
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The goal of this project is to introduce and present a machine learning
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I am presenting the PhD thesis that I have found useful and interesting
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