Few-Shot Learning with Metric-Agnostic Conditional Embeddings. - Nathan Hilliard; Lawrence Phillips; Scott Howland; Artëm Yankov; Courtney D. Corley; Nathan O. Hodas (CoRR, 2018)

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. - Henry Kvinge; Scott Howland; Nico Courts; Lauren A. Phillips; John Buckheit; Zachary New; Elliott Skomski; Jung H. Lee; Sandeep Tiwari; Jessica Hibler; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

A Topological-Framework to Improve Analysis of Machine Learning Model Performance. - Henry Kvinge; Colby Wight; Sarah Akers; Scott Howland; Woongjo Choi; Xiaolong Ma; Luke J. Gosink; Elizabeth Jurrus; Keerti Kappagantula; Tegan H. Emerson (CoRR, 2021)

Adaptive Transfer Learning: a simple but effective transfer learning. - Jung H. Lee; Henry J. Kvinge; Scott Howland; Zachary New; John Buckheit; Lauren A. Phillips; Elliott Skomski; Jessica Hibler; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

TopTemp: Parsing Precipitate Structure from Temper Topology. - Lara Kassab; Scott Howland; Henry Kvinge; Keerti Sahithi Kappagantula; Tegan Emerson (TAG-ML, 2022)

Recursive Decoding: A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding. - Matthew Setzler; Scott Howland; Lauren A. Phillips (CoRR, 2022)

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing. - Scott Howland; Lara Kassab; Keerti Kappagantula; Henry Kvinge; Tegan Emerson (CoRR, 2023)

Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry. - Anurag Acharya 002; Shivam Sharma; Robin Cosbey; Megha Subramanian; Scott Howland; Maria Glenski (CoRR, 2024)