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Found 7 papers, 1 papers with code
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Learning Abstract Task Representations
A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation.
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment.
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
Transfer Learning in Astronomy: A New Machine-Learning Paradigm
In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks.
A General Approach to Domain Adaptation with Applications in Astronomy
The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications.
Conceptual Domain Adaptation Using Deep Learning
We introduce a search framework to correctly align high-level representations when training deep networks; such framework leads to the notion of conceptual --as opposed to representational-- domain adaptation.
