123 citations · 359 across the 16 of their papers we have counts for
4 papers · 1 filter
Transfer Learning in Astronomy: A New Machine-Learning Paradigm
Ricardo Vilalta
The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any…
A General Approach to Domain Adaptation with Applications in Astronomy
Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber +1
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. The problem is generally…
Stress testing the dark energy equation of state imprint on supernova data
Ben Moews, Rafael S. de Souza, Emille E. O. Ishida +4
This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-d…
Conceptual Domain Adaptation Using Deep Learning
Behrang Mehrparvar, Ricardo Vilalta
Deep learning has recently been shown to be instrumental in the problem of domain adaptation, where the goal is to learn a model on a target domain using a similar --but not identi…