# Astronomy

88 papers with code • 1 benchmarks • 2 datasets

## Most implemented papers

# Self-Normalizing Neural Networks

We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.

# Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.

# Deep-Learnt Classification of Light Curves

As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves.

# Extracting the main trend in a dataset: the Sequencer algorithm

However, some are challenging to detect as they may be expressed in complex manners.

# Astronomical image reconstruction with convolutional neural networks

State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem.

# Deep Convolutional Denoising of Low-Light Images

Poisson distribution is used for modeling noise in photon-limited imaging.

# Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph

Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples.

# Efficient Optimization of Echo State Networks for Time Series Datasets

Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.

# Convolutional neural networks: a magic bullet for gravitational-wave detection?

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.

# Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year.