3 papers
cs.AR2026
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +3
This report extends our previous work (Part 1), which introduced an energy-based model for learning and decision-making under uncertainty. The model leverages stochastic Langevin d…
cs.AI2026
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2
Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertaint…
stat.ML2024★ 1 cited
Generative vs. Discriminative modeling under the lens of uncertainty quantification
Elouan Argouarc'h, François Desbouvries, Eric Barat +1
Learning a parametric model from a given dataset indeed enables to capture intrinsic dependencies between random variables via a parametric conditional probability distribution and…