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Parametric Rectified Power Sigmoid Units: Learning Nonlinear Neural Transfer Analytical Forms
Parameters of this neural transfer class are considered as learnable for the sake of discovering the complex shapes that can contribute in solving machine learning issues.
Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters
Experimental results show that our two algorithms are competitive in terms of clustering performance with respect to K-means and DB-Scan, while lowering by a factor at least $2$ the amount of data exchanged between sensors.
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.
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