A Self-Supervised Mixed-Curvature Graph Neural Network

Attention-based Graph Neural Network for Semi-supervised Learning

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a fully-connected layer. Perhaps surprisingly, we show that...

Self-Organizing Graphs - A Neural Network Perspective of Graph Layout

The paper presents self-organizing graphs, a novel approach to graph layout based on a competitive learning algorithm. This method is an extension of self-organization strategies known from unsupervised neural networks, namely from Kohonen's self-organizing map. Its main advantage is that it is very exibly adaptable to arbitrary types of visualization spaces, for it is explicitly parameterized ...

Neural Network Based Supervised Self Organizing Maps for Face Recognition

The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named S...

Curvature Estimation with a DCA Neural Network

Curvature has been identiied as an important feature to reconstruct properties like object shape or relative depth from two-dimensional gray scale images 6, 9]. This coincides with the assumption that curvature is processed by a separate channel in human early vision, just like contours or contrast 23, 2, 3]. The process of early vision is assumed to be divided into vision modules 15] that are ...

A supervised radial basis function neural network