44 citations · 106 across the 15 of their papers we have counts for
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quant-ph2021★ 8 cited
Toward Trainability of Deep Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase. This re…
cs.LG2021★ 1 cited
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Weigang Lu, Yibing Zhan, Binbin Lin +6
Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing…
cs.CV2021★ 3 cited
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Ye Huang, Di Kang, Wenjing Jia +2
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. H…