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60 Publications visible to you, out of a total of 60
Semi-Automated Morphological Classification of All Radio Galaxies in the FIRST Survey Catalog Including Near-IR Counterparts in UKIDSS

Abstract

Not specified

Author: Erica Hopkins

Date Published: 1st Oct 2019

Publication Type: Master's Thesis

Mixed Variational Inference

Abstract (Expand)

The Laplace approximation has been one of the workhorses of Bayesian inference. It often delivers good approximations in practice despite the fact that it does not strictly take into account where the … volume of posterior density lies. Variational approaches avoid this issue by explicitly minimising the Kullback-Leibler divergence DKL between a postulated posterior and the true (unnormalised) logarithmic posterior. However, they rely on a closed form DKL in order to update the variational parameters. To address this, stochastic versions of variational inference have been devised that approximate the intractable DKL with a Monte Carlo average. This approximation allows calculating gradients with respect to the variational parameters. However, variational methods often postulate a factorised Gaussian approximating posterior. In doing so, they sacrifice a-posteriori correlations. In this work, we propose a method that combines the Laplace approximation with the variational approach. The advantages are that we maintain: applicability on non-conjugate models, posterior correlations and a reduced number of free variational parameters. Numerical experiments demonstrate improvement over the Laplace approximation and variational inference with factorised Gaussian posteriors.

Author: Nikolaos Gianniotis

Date Published: 1st Jul 2019

Publication Type: InCollection

Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning

Abstract (Expand)

The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. … The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond.

Author: Antonio D'Isanto

Date Published: 1st Feb 2019

Publication Type: Doctoral Thesis

Efficient Optimization of Echo State Networks for Time Series Datasets

Abstract

Not specified

Authors: Jacob Reinier Maat, Nikos Gianniotis, Pavlos Protopapas

Date Published: 1st Jul 2018

Publication Type: InProceedings

Photometric redshift estimations

Abstract

Not specified

Author: Kai L. Polsterer

Date Published: 2017

Publication Type: InProceedings

Linear Dimensionality Reduction for Time Series

Abstract (Expand)

Visualisation by dimensionality reduction is an important tool for data exploration. In this work we are interested in visualising time series. To that end we formulate a latent variable model that … mirrors probabilistic principal component analysis (PPCA). However, as opposed to PPCA which maps the latent variables directly to the data space, we first map the latent variables to the parameter space of a recurrent neural network, i.e. each latent projection instantiates a recurrent network. Each instantiated recurrent network in turn is responsible for modelling a time series in the dataset. Hence, each latent variable is indirectly mapped to a time series. Incorporating the recurrent network in the latent variable model helps us account for the temporal nature of the time series and capture their underlying dynamics. The proposed algorithm is demonstrated on two benchmark problems and a real world dataset.

Author: Nikolaos Gianniotis

Date Published: 2017

Publication Type: InProceedings

A spectral model for multimodal redshift estimation

Abstract

Not specified

Authors: Sven D. Kugler, Nikolaos Gianniotis, Kai L. Polsterer

Date Published: 1st Dec 2016

Publication Type: InProceedings

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