Heterogeneous Graph Neural Network With Multi-View Representation Learning

Learning classifier systems with neural network representation

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network

On Deep Multi-View Representation Learning

We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for representation learning while only one view is available at test time. Previous work on this problem has proposed several techniques based on deep neural networks, typically involving either autoencoderlike networks with a reconstruction objective or paired feedforw...

Multi-view, Multi-label Learning with Deep Neural Networks

Deep learning is a popular technique in modern online and offline services. Deep neural network based learning systems have made groundbreaking progress in model size, training and inference speed, and expressive power in recent years, but to tailor the model to specific problems and exploit data and problem structures is still an ongoing research topic. We look into two types of deep ‘‘multi-’...

Learning Human Identity Using View-Invariant Multi-view Movement Representation

In this paper a novel view-invariant human identification method is presented. A multi-camera setup is used to capture the human body from different observation angles. Binary body masks from all the cameras are concatenated to produce the so-called multi-view binary masks. These masks are rescaled and vectorized to create feature vectors in the input space. A view-invariant human body represen...