50 citations · 101 across the 16 of their papers we have counts for
18 papers
The LSST AGN Data Challenge: Selection methods
Đorđe V. Savić, Isidora Jankov, Weixiang Yu +12
Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that a…
Deep learning of quasar lightcurves in the LSST era
Andjelka B. Kovacevic, Dragana Ilic, Luka C. Popovic +6
Deep learning techniques are required for the analysis of synoptic (multi-band and multi-epoch) light curves in massive data of quasars, as expected from the Vera C. Rubin Observat…
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar +3
Tabular data is often hidden in text, particularly in medical diagnostic reports. Traditional machine learning (ML) models designed to work with tabular data, cannot effectively pr…
From Data to Software to Science with the Rubin Observatory LSST
Katelyn Breivik, Andrew J. Connolly, K. E. Saavik Ford +97
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the…
Intrinsically motivated option learning: a comparative study of recent methods
Djordje Božić, Predrag Tadić, Mladen Nikolić
Options represent a framework for reasoning across multiple time scales in reinforcement learning (RL). With the recent active interest in the unsupervised learning paradigm in the…
Neural Algorithmic Reasoners are Implicit Planners
Andreea Deac, Petar Veličković, Ognjen Milinković +3
Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit pl…