3 citations · 3 across the 3 of their papers we have counts for
3 papers
A Theory of Multi-Agent Generative Flow Networks
Leo Maxime Brunswic, Haozhi Wang, Shuang Luo +3
Generative flow networks utilize a flow-matching loss to learn a stochastic policy for generating objects from a sequence of actions, such that the probability of generating a patt…
Ergodic Generative Flows
Leo Maxime Brunswic, Mateo Clemente, Rui Heng Yang +3
Generative Flow Networks (GFNs) were initially introduced on directed acyclic graphs to sample from an unnormalized distribution density. Recent works have extended the theoretical…
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
Mahdi Beitollahi, Alex Bie, Sobhan Hemati +4
In one-shot federated learning (FL), clients collaboratively train a global model in a single round of communication. Existing approaches for one-shot FL enhance communication effi…