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Found 8 papers, 1 papers with code
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
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.
TopTemp: Parsing Precipitate Structure from Temper Topology
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements.
Recursive Decoding: A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding
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.
Adaptive Transfer Learning: a simple but effective transfer learning
Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
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.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
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.
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
