The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

Graph deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …

A number of problems can be formulated as prediction on graph-structured data. In this
work, we generalize the convolution operator from regular grids to arbitrary graphs while …

Unlike on images, semantic learning on 3D point clouds using a deep network is
challenging due to the naturally unordered data structure. Among existing works, PointNet …

Exploiting graph-structured data has many real applications in domains including natural
language semantics, programming language processing, and malware analysis. A variety of …

We generalize the scattering transform to graphs and consequently construct a
convolutional neural network on graphs. We show that under certain conditions, any feature …

Collective classification has attracted considerable attention in the last decade, where the
labels within a group of instances are correlated and should be inferred collectively, instead …

Implementing algorithms on a fault-tolerant quantum computer will require fast decoding
throughput and latency times to prevent an exponential increase in buffer times between the …

We propose a neural machine-reading model that constructs dynamic knowledge graphs
from procedural text. It builds these graphs recurrently for each step of the described …