183,966 research outputs found
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
Inspired by the hierarchical cognitive architecture and the perception-action
model (PAM), we propose that the internal status acts as a kind of
common-coding representation which affects, mediates and even regulates the
sensorimotor behaviours. These regulation can be depicted in the Bayesian
framework, that is why cognitive agents are able to generate behaviours with
subtle differences according to their emotion or recognize the emotion by
perception. A novel recurrent neural network called recurrent neural network
with parametric bias units (RNNPB) runs in three modes, constructing a
two-level emotion regulated learning model, was further applied to testify this
theory in two different cases.Comment: Accepted at The 5th International Conference on Data-Driven Control
and Learning Systems. 201
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
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