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cs.LG2022★ 1 cited
On the optimization and pruning for Bayesian deep learning
Xiongwen Ke, Yanan Fan
The goal of Bayesian deep learning is to provide uncertainty quantification via the posterior distribution. However, exact inference over the weight space is computationally intrac…
cs.LG2019
Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma +3
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs…