8 citations · 8 across the 3 of their papers we have counts for
4 papers
Steering Generative Models for Protein Design: Aligning and Conditioning Strategies
Filippo Stocco, Michele Garibbo, Noelia Ferruz
Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins a…
Reinforcement Learning Guides Generative Protein Language Models
Filippo Stocco, Maria Artigues-Lleixa, Andrea Hunklinger +4
Protein engineering can optimize molecules for biotechnology and therapeutics, but navigating the high-dimensional sequence landscape remains challenging. Protein language models (…
Taylor TD-learning
Michele Garibbo, Maxime Robeyns, Laurence Aitchison
Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn a critic. However, TD-learning updates can be high variance. Here, we introduce a model-ba…
What deep reinforcement learning tells us about human motor learning and vice-versa
Michele Garibbo, Casimir Ludwig, Nathan Lepora +1
Machine learning and specifically reinforcement learning (RL) has been extremely successful in helping us to understand neural decision making processes. However, RL's role in unde…