In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules
provide interpretable explanations when used for prediction as well as being able to …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-
cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes …

Probabilistic Logic Programming extends Logic Programming by enabling the
representation of uncertain information by means of probability theory. Probabilistic Logic …

We propose a method to combine the interpretability and expressive power of first-order
logic with the effectiveness of neural network learning. In particular, we introduce a lifted …

This paper aims to leverage symbolic knowledge to improve the performance and
interpretability of the Visual Relationship Detection (VRD) models. Existing VRD methods …

Lifted graphical models provide a language for expressing dependencies between different
types of entities, their attributes, and their diverse relations, as well as techniques for …

This is an introduction to multicast routing, which is the study of methods for routing from one
source to many destinations, or from many sources to many destinations. Multicast is …

Judging by the increasing impact of machine learning on large-scale data analysis in the
last decade, one can anticipate a substantial growth in diversity of the machine learning …