136 citations · 137 across the 8 of their papers we have counts for
8 papers
Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry
Anurag Acharya, Shivam Sharma, Robin Cosbey +3
A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natu…
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
Scott Howland, Lara Kassab, Keerti Kappagantula +2
The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence c…
TopTemp: Parsing Precipitate Structure from Temper Topology
Lara Kassab, Scott Howland, Henry Kvinge +2
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and ev…
Recursive Decoding: A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding
Matthew Setzler, Scott Howland, Lauren Phillips
Compositional generalization is a troubling blind spot for neural language models. Recent efforts have presented techniques for improving a model's ability to encode novel combinat…
Adaptive Transfer Learning: a simple but effective transfer learning
Jung H Lee, Henry J Kvinge, Scott Howland +7
Transfer learning (TL) leverages previously obtained knowledge to learn new tasks efficiently and has been used to train deep learning (DL) models with limited amount of data. When…
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Henry Kvinge, Colby Wight, Sarah Akers +7
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 p…
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
Henry Kvinge, Scott Howland, Nico Courts +9
The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabel…
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Nathan Hilliard, Lawrence Phillips, Scott Howland +3
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architec…