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Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski +4
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large n…
Spectral Complexity-scaled Generalization Bound of Complex-valued Neural Networks
Haowen Chen, Fengxiang He, Shiye Lei +1
Complex-valued neural networks (CVNNs) have been widely applied to various fields, especially signal processing and image recognition. However, few works focus on the generalizatio…
Hierarchical Prototype Networks for Continual Graph Representation Learning
Xikun Zhang, Dongjin Song, Dacheng Tao
Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e.…
Off-policy Imitation Learning from Visual Inputs
Zhihao Cheng, Li Shen, Dacheng Tao
Recently, various successful applications utilizing expert states in imitation learning (IL) have been witnessed. However, another IL setting -- IL from visual inputs (ILfVI), whic…
Effective and Efficient Graph Learning for Multi-view Clustering
Quanxue Gao, Wei Xia, Xinbo Gao +3
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering…
Commutative Lie Group VAE for Disentanglement Learning
Xinqi Zhu, Chang Xu, Dacheng Tao
We view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. Traditionally, such a structure is fixe…