Search Results for author: Gabriella Contardo

Found 13 papers, 7 papers with code

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients

1 code implementation22 Mar 2021 V. Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin

There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.

Anomaly Detection

Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network

1 code implementation8 Jul 2020 Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo

This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.

Anomaly Detection Astronomy +3

Dalek -- a deep-learning emulator for TARDIS

no code implementations3 Jul 2020 Wolfgang E. Kerzendorf, Christian Vogl, Johannes Buchner, Gabriella Contardo, Marc Williamson, Patrick van der Smagt

We show that we can train an emulator for this problem given a modest training set of a hundred thousand spectra (easily calculable on modern supercomputers).

Time Series

From Dark Matter to Galaxies with Convolutional Neural Networks

1 code implementation17 Oct 2019 Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho

Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.

The Quijote simulations

3 code implementations11 Sep 2019 Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, Elena Giusarma, Yin Li, Erwan Allys, Antoine Brochard, Chi-Ting Chiang, Siyu He, Alice Pisani, Andrej Obuljen, Yu Feng, Emanuele Castorina, Gabriella Contardo, Christina D. Kreisch, Andrina Nicola, Roman Scoccimarro, Licia Verde, Matteo Viel, Shirley Ho, Stephane Mallat, Benjamin Wandelt, David N. Spergel

The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.

Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics

From Dark Matter to Galaxies with Convolutional Networks

1 code implementation15 Feb 2019 Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho

In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.

A Meta-Learning Approach to One-Step Active Learning

no code implementations26 Jun 2017 Gabriella Contardo, Ludovic Denoyer, Thierry Artieres

More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot.

Active Learning Meta-Learning

Sequential Cost-Sensitive Feature Acquisition

no code implementations13 Jul 2016 Gabriella Contardo, Ludovic Denoyer, Thierry Artières

We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost.

reinforcement-learning reinforcement Learning +1

Representation Learning for cold-start recommendation

no code implementations22 Dec 2014 Gabriella Contardo, Ludovic Denoyer, Thierry Artieres

Representations for both users and items are computed from the observed ratings and used for prediction.

Collaborative Filtering Representation Learning

Learning States Representations in POMDP

no code implementations20 Dec 2013 Gabriella Contardo, Ludovic Denoyer, Thierry Artieres, Patrick Gallinari

We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.

Cannot find the paper you are looking for? You can Submit a new open access paper.