Add to ORKGWith convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems. Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be efficiently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references. Beyond the graph applications, ...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Significant strides have been made in computer vision over the past few years due to the recent deve...
In 2006, Geoffry Hinton published a paper showing how to train a neural network capable of recognizi...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
This thesis presents two principled approaches to improve the performance of convolutional neural ne...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real worl...
Computational visual perception, also known as computer vision, is a field of artificial intelligenc...
This paper proposes a pre-training method for neural network-based character recognizers to reduce t...
Deep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Graphs are a powerful way to model network data with the objects as nodes and the relationship betwe...
Deep neural network models have established themselves as an unparalleled force in the domains of v...
Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learn...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Significant strides have been made in computer vision over the past few years due to the recent deve...
In 2006, Geoffry Hinton published a paper showing how to train a neural network capable of recognizi...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
This thesis presents two principled approaches to improve the performance of convolutional neural ne...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real worl...
Computational visual perception, also known as computer vision, is a field of artificial intelligenc...
This paper proposes a pre-training method for neural network-based character recognizers to reduce t...
Deep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Graphs are a powerful way to model network data with the objects as nodes and the relationship betwe...
Deep neural network models have established themselves as an unparalleled force in the domains of v...
Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learn...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Significant strides have been made in computer vision over the past few years due to the recent deve...
In 2006, Geoffry Hinton published a paper showing how to train a neural network capable of recognizi...