activity
20182021
most citedAn Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

67 citations · 71 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20211 cited

On Invariance Penalties for Risk Minimization

Kia Khezeli, Arno Blaas, Frank Soboczenski +2

The Invariant Risk Minimization (IRM) principle was first proposed by Arjovsky et al. [2019] to address the domain generalization problem by leveraging data heterogeneity from diff…

cs.LG20213 cited

Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft

Athanasios Vlontzos, Gabriel Sutherland, Siddha Ganju +1

Future short or long-term space missions require a new generation of monitoring and diagnostic systems due to communication impasses as well as limitations in specialized crew and…

cs.CL2020

Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

Byron C. Wallace, Sayantan Saha, Frank Soboczenski +1

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarizat…

astro-ph.EP201967 cited

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Adam D. Cobb, Michael D. Himes, Frank Soboczenski +7

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the pote…

astro-ph.EP2018

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne +8

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet…