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Found 5 papers, 1 papers with code
Multi-stage feature decorrelation constraints for improving CNN classification performance
Considering that there are many layers in CNN, through experimental comparison and analysis, MFD Loss acts on multiple front layers of CNN, constrains the output features of each layer and each channel, and performs supervision training jointly with classification loss function during network training.
CHIEF: Clustering with Higher-order Motifs in Big Networks
In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.
Searching Personalized $k$-wing in Large and Dynamic Bipartite Graphs
By exploiting the discoveries, we propose novel algorithms for maintaining the two indices, which substantially reduces the cost of maintenance.
Index-based Solutions for Efficient Density Peak Clustering
Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space.
Predicting Destinations by a Deep Learning based Approach
However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations.
