ABSTRACT Batch processing of dynamic graphs is a very common technique for a variety of applications, such as computer vision and natural language processing. However, due to the varieties of type and ...

NeuGraph introduces graph computation optimizations into the management of data partitioning, scheduling, and parallelism in dataflow-based deep learning frameworks. Our evaluation shows that, on ...

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams ...

Deep Learning for Graphs Has a Long-Standing History. The deep learning for graphs field is rooted in neural networks for graphs research and early 1990s works on Recursive Neural Networks (RecNN) for ...

Dragon is a C(Computation)G(Graph)V(Virtual)M(Machine) based distributed deep learning framework. Our goal is to reduce the unnecessary structures or interfaces. Therefore, in addition to feed or ...

Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in ...

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams ...