4 papers
A Malliavin calculus approach to score functions in diffusion generative models
Ehsan Mirafzali, Frank Proske, Utkarsh Gupta +2
Score-based diffusion generative models have recently emerged as a powerful tool for modelling complex data distributions. These models aim at learning the score function, which de…
Malliavin Calculus for Score-based Diffusion Models
Ehsan Mirafzali, Utkarsh Gupta, Patrick Wyrod +3
We introduce a new framework based on Malliavin calculus to derive exact analytical expressions for the score function , i.e., the gradient of the log-density a…
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske +1
Distributed methods are essential for handling machine learning pipelines comprising large-scale models and datasets. However, their benefits often come at the cost of increased co…
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov +3
Despite the vast empirical evidence supporting the efficacy of adaptive optimization methods in deep learning, their theoretical understanding is far from complete. This work intro…