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Found 8 papers, 3 papers with code
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically.
Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
We offer three main contributions: 1) Based on temporal modulation and attention mechanisms, we propose a Deep attention model (TimeModAttn) to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods.
Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker.
Transformation Based Deep Anomaly Detection in Astronomical Images
In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform).
On the Information Plane of Autoencoders
Recently, the Information Plane (IP) was proposed to analyze them, which is based on the information-theoretic concept of mutual information (MI).
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes.
Ranked #1 on
Spindle Detection
on MASS SS2
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS).
A Review of Feature Selection Methods Based on Mutual Information
In this work we present a review of the state of the art of information theoretic feature selection methods.
