From the 1 of 5 linked papers with an AI index.
5 papers
From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models
Kaitlin Gili
We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classificatio…
Inherent interpretability provides inherent value in quantum machine learning
Kaitlin Gili, Zachary P. Bradshaw
The paper argues that the intrinsic mathematical structure of quantum machine learning models can provide inherent interpretability, offering value beyond raw performance, and illu…
Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
Kaitlin Gili, Mainak Nistala, Kristen Wendell +1
STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team…
Combining physics education and machine learning research to measure evidence of students' mechanistic sensemaking
Kaitlin Gili, Kyle Heuton, Astha Shah +2
Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students' mechani…
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
Michael T. Wojnowicz, Kaitlin Gili, Preetish Rath +7
We seek a computationally efficient model for a collection of time series arising from multiple interacting entities (a.k.a. "agents"). Recent models of temporal patterns across in…