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79 Publications visible to you, out of a total of 79
Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

Abstract

Not specified

Authors: A. D’Isanto, K. L. Polsterer

Date Published: 2018

Publication Type: Journal

Return of the features - Efficient feature selection and interpretation for photometric redshifts

Abstract

Not specified

Authors: A. D\’Isanto, S. Cavuoti, F. Gieseke, K. L. Polsterer

Date Published: 2018

Publication Type: Journal

Massively-parallel best subset selection for ordinary least-squares regression

Abstract

Not specified

Authors: Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes

Date Published: 1st Nov 2017

Publication Type: InProceedings

Astroinformatics; a new discipline or business as usual?

Abstract

Not specified

Author: K. L. Polsterer

Date Published: 1st Sep 2017

Publication Type: InProceedings

Generalised model-independent characterisation of strong gravitational lenses. I. Theoretical foundations

Abstract

Not specified

Author: J. Wagner

Date Published: 1st May 2017

Publication Type: Journal

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

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