Search Results for author:
Found 6 papers, 3 papers with code
A representation learning approach to probe for dynamical dark energy in matter power spectra
We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure.
A robust estimator of mutual information for deep learning interpretability
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models.
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cosmological surveys.
Discovering the building blocks of dark matter halo density profiles with neural networks
The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history.
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for $\textit{any}$ source mechanism.
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy
In this work, our contributions are two-fold: first, by adapting state-of-the-art techniques from representation learning, we introduce a novel approach to learning LDP mechanisms.
