17 citations · 17 across the 5 of their papers we have counts for
6 papers
Learning -Powered Conditional Distributions with Importance-Weighted Flow Matching
Maciej Falkiewicz, Frantzeska Lavda, Jason Ramapuram +1
Classifier-Free Guidance (CFG) with guidance scale is commonly motivated by the -powered conditional distribution obtained by setting , but CFG does not, in general, sa…
Climate Physics Dynamic Matching
Gurjeet Sangra Singh, Frantzeska Lavda, Alexandros Kalousis
Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as black boxes that neglect underly…
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,…
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda +1
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre pe…
Improving VAE generations of multimodal data through data-dependent conditional priors
Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. Thi…
Continual Classification Learning Using Generative Models
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova +1
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple task…