4 accepted papers
Traditional parameter-space posterior inference for Bayesian neural networks faces several challenges, such as the difficulty in specifying meaningful prior, the potential pathologies in deep models and the intractability for multi-modal posterior. To address these issues, functional variational inf…
Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and pathological behaviours in deep networks, which can lead to improper posterior inference. To address these issues, functional B…
Bayesian deep learning (BDL) is an emerging field that combines the strong function approximation power of deep learning with the uncertainty modeling capabilities of Bayesian methods. In addition to those virtues, however, there are accompanying issues brought by such a combination to the classical…
Variational policy optimization has become increasingly attractive to the reinforcement learning community because of its strong capability in uncertainty modeling and environment generalization. However, almost all existing studies in this area rely on Kullback{–}Leibler (KL) divergence which is un…