57 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Self-Normalizing Neural Networks

bioinf-jku/SNNs NeurIPS 2017

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

tpospisi/rfcde 30 Aug 2019

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

hombit/light-curve 19 Sep 2017

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

dalya/Sequencer 24 Jun 2020

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

Astronomical image reconstruction with convolutional neural networks

rflamary/AstroImageReconsCNN 14 Dec 2016

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

TalRemez/deep_class_aware_denoising 6 Jan 2017

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

Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph

j-bac/elpigraph-python 20 Apr 2018

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

blindedjoy/RcTorch 12 Mar 2019

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?

timothygebhard/magic-bullet 18 Apr 2019

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

hagabbar/vitamin_b 13 Sep 2019

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.