2 citations · 3 across the 5 of their papers we have counts for
6 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…
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
Pedro Dall'Antonia, Tiago da Silva, Daniel Csillag +2
Generative Flow Networks (GFlowNets) are a flexible family of amortized samplers trained to generate discrete and compositional objects with probability proportional to a reward fu…
Streaming Bayes GFlowNets
Tiago da Silva, Daniel Augusto de Souza, Diego Mesquita
Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch whenever new data arrives. In principle, Bayesia…
On Divergence Measures for Training GFlowNets
Tiago da Silva, Eliezer de Souza da Silva, Diego Mesquita
Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative mode…
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…
Expert-Aided Causal Discovery of Ancestral Graphs
Tiago da Silva, Bruna Bazaluk, Eliezer de Souza da Silva +6
Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To m…