5 papers
Learning a Mixture of GFlowNets
Tiago da Silva, Amauri H. Souza, Salem Lahlou
Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space exploration and convergence than that…
Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective
André Ribeiro, Germano Barcelos, Amauri H. Souza +2
Graph Neural Networks (GNNs) often struggle to capture long-range dependencies due to over-squashing -- a phenomenon in which the repeated compression of node embeddings into finit…
Differentiable Lifting for Topological Neural Networks
Jorge Luiz Franco, Gabriel Duarte, Alexander Nikitin +3
Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message-passing neural networks. In…
Cooperative Sheaf Neural Networks
André Ribeiro, Ana Luiza Tenório, Juan Belieni +2
Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophilic data and avoid oversmoothing…
Embarrassingly Parallel GFlowNets
Tiago da Silva, Luiz Max Carvalho, Amauri Souza +2
GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. Training GFlowNets requires repeated evaluations of the unnormalized target dist…