Search Results for author:
Found 13 papers, 7 papers with code
Learning States Representations in POMDP
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.
Representation Learning for cold-start recommendation
Representations for both users and items are computed from the observed ratings and used for prediction.
Sequential Cost-Sensitive Feature Acquisition
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost.
A Meta-Learning Approach to One-Step Active Learning
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.
From Dark Matter to Galaxies with Convolutional Networks
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.
The Quijote simulations
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 Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
Dalek -- a deep-learning emulator for TARDIS
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).
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
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.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
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.
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
