Search Results for author:
Found 10 papers, 2 papers with code
Question answering systems for health professionals at the point of care -- a systematic review
Results: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods.
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of significant distance and duration.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
In this work, we augment small human medical datasets with in-vitro data and animal models.
On Invariance Penalties for Risk Minimization
We discuss the role of its eigenvalues in the relationship between the risk and the invariance penalty, and demonstrate that it is ill-conditioned for said counterexamples.
Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft
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 equipment.
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual.
Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer
Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy.
Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra.
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3, 000, 000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator.
