5 papers
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona
Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological process…
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…
Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization
Adrian Martens, Mathias Neufang, Alessandro Butté +3
Bioprocesses are central to modern biotechnology, enabling sustainable production in pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing…
Paying Alignment Tax with Contrastive Learning
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +1
Current debiasing approaches often result a degradation in model capabilities such as factual accuracy and knowledge retention. Through systematic evaluation across multiple benchm…
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…