We have 2 of 11 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
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 of a monolithic sampler. However, these methods often add a substantial runtime overhead to the base model, and their conceptual connection...
This work proposes $\partial$lift (DiffLift), a general framework for learning graph liftings to hypergraphs and cellular- and simplicial complexes in an end-to-end fashion and shows that $\partial$lift outperforms existing lifting methods on multiple benchmarks for graph and node classification across different TNN ar...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
