1 citations · 1 across the 3 of their papers we have counts for
3 papers
SoDaDE: Solvent Data-Driven Embeddings with Small Transformer Models
Gabriel Kitso Gibberd, Jose Pablo Folch, Antonio Del Rio Chanona
Computational representations have become crucial in unlocking the recent growth of machine learning algorithms for chemistry. Initially hand-designed, machine learning has shown t…
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +3
Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it t…
Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
Edmund Judge, Mohammed Azzouzi, Austin M. Mroz +2
Multi fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectivel…