14 citations · 19 across the 9 of their papers we have counts for
5 papers · 1 filter
Why Machine Learning Models Systematically Underestimate Extreme Values II: How to Fix It with LatentNN
Yuan-Sen Ting
Attenuation bias -- the systematic underestimation of regression coefficients due to measurement errors in input variables -- affects astronomical data-driven models. For linear re…
Deep Learning in Astrophysics
Yuan-Sen Ting
Deep learning has generated diverse perspectives in astronomy, with ongoing discussions between proponents and skeptics motivating this review. We examine how neural networks compl…
Statistical Machine Learning for Astronomy -- A Textbook
Yuan-Sen Ting
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that…
Teaching Astronomy with Large Language Models
Yuan-Sen Ting, Teaghan O'Briain
We present a study of LLM integration in final-year undergraduate astronomy education, examining how students develop AI literacy through structured guidance and documentation requ…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…