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Computational Capabilities Of Graph Neural Networks, Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini
Computational Capabilities Of Graph Neural Networks, Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini
Faculty of Informatics - Papers (Archive)
In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property …
Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum
Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Informatics - Papers (Archive)
Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian patterns. Among several advantages, the CNN integrates feature extraction and classification into one single, fully adaptive structure. It can extract two-dimensional features at increasing scales, and it is …
A Neural Network Pruning Approach Based On Compressive Sampling, Abdesselam Bouzerdoum, Son Lam Phung, Jie Yang
A Neural Network Pruning Approach Based On Compressive Sampling, Abdesselam Bouzerdoum, Son Lam Phung, Jie Yang
Faculty of Informatics - Papers (Archive)
The balance between computational complexity and the architecture bottlenecks the development of Neural Networks (NNs), An architecture that is too large or too small will influence the performance to a large extent in terms of generalization and computational cost. In the past, saliency analysis has been employed to determine the most suitable structure, however, it is time-consuming and the performance is not robust. In this paper, a family of new algorithms for pruning elements (weighs and hidden neurons) in Neural Networks is presented based on Compressive Sampling (CS) theory. The proposed framework makes it possible to locate the significant elements, …