1 citations · 1 across the 6 of their papers we have counts for
6 papers
Variational Grey-Box Dynamics Matching
Gurjeet Sangra Singh, Frantzeska Lavda, Giangiacomo Mercatali +1
Deep generative models such as flow matching and diffusion models have shown great potential in learning complex distributions and dynamical systems, but often act as black-boxes,…
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas +1
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or…
Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
Giangiacomo Mercatali, Andre Freitas, Jie Chen
Interacting systems are prevalent in nature. It is challenging to accurately predict the dynamics of the system if its constituent components are analyzed independently. We develop…
MING: A Functional Approach to Learning Molecular Generative Models
Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali +1
Traditional molecule generation methods often rely on sequence- or graph-based representations, which can limit their expressive power or require complex permutation-equivariant ar…
Learning Disentangled Representations for Natural Language Definitions
Danilo S. Carvalho, Giangiacomo Mercatali, Yingji Zhang +1
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing…
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders
Giangiacomo Mercatali, André Freitas
The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled. Most approaches…