1 code implementation • BigScience (ACL) 2022 • Sameera Horawalavithana, Ellyn Ayton, Shivam Sharma, Scott Howland, Megha Subramanian, Scott Vasquez, Robin Cosbey, Maria Glenski, Svitlana Volkova
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
no code implementations • 13 Jan 2023 • Scott Howland, Lara Kassab, Keerti Kappagantula, Henry Kvinge, Tegan Emerson
By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples.
no code implementations • 1 Apr 2022 • Lara Kassab, Scott Howland, Henry Kvinge, Keerti Sahithi Kappagantula, Tegan Emerson
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements.
no code implementations • 27 Jan 2022 • Matthew Setzler, Scott Howland, Lauren Phillips
Here we focus on this latter "decode-side" form of generalization in the context of gSCAN, a synthetic benchmark for compositional generalization in grounded language understanding.
no code implementations • 22 Nov 2021 • 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
Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.
no code implementations • 9 Jul 2021 • Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke Gosink, Elizabeth Jurrus, Keerti Kappagantula, Tegan H. Emerson
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model performance has become increasingly problematic.
no code implementations • 2 Jun 2021 • 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
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
no code implementations • 12 Feb 2018 • Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, Nathan O. Hodas
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.