Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Numerous important problems can be framed as learning from graph data. We propose a
framework for learning convolutional neural networks for arbitrary graphs. These graphs …

A fundamental challenge in developing high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

We propose to study equivariance in deep neural networks through parameter symmetries.
In particular, given a group G that acts discretely on the input and output of a standard neural …

Many representation schemes combining first-order logic and probability have been
proposed in recent years. Progress in unifying logical and probabilistic inference has been …

Dependency networks approximate a joint probability distribution over multiple random
variables as a product of conditional distributions. Relational Dependency Networks (RDNs) …

Topic models have been used successfully for a variety of problems, often in the form of
application-specific extensions of the basic Latent Dirichlet Allocation (LDA) model. Because …

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 …

Statistical Relational Learning (SRL) is a subarea of machine learning which combines
elements from statistical and probabilistic modeling with languages which support structured …