Blogs

I am delighted to announce that our paper, When do GFlowNets Learn the Right Distribution?, has been accepted for presentation at ICLR 2025! This work advances our theoretical understanding of Generative Flow Networks (GFlowNets) by examining the impact of balance violations on their ability to approximate target distributions and proposing a novel metric for assessing correctness.

I am thrilled to share that two of our papers have been accepted at the LatinX in AI Research Workshop at NeurIPS 2024! These works push the boundaries in causal inference and generative modeling, contributing new methodologies and insights to the field.

I’m excited to announce that our paper, “On Divergence Measures for Training GFlowNets,” authored by Tiago da Silva, Eliezer de Souza da Silva, and Diego Mesquita, has been accepted at NeurIPS 2024!

There is an algorithm to generate Dirichlet samples using a sampler for Gamma distribution for any \( \alpha > 0 \) and \( \beta > 0 \).