Effectively compressing and optimizing tensor networks requires reliable methods for fixing
the latent degrees of freedom of the tensors, known as the gauge. Here we introduce a new …

Most of the successful deep neural network architectures are structured, often consisting of
elements like convolutional neural networks and gated recurrent neural networks. Recently …

Belief propagation (BP) can be a useful tool to approximately contract a tensor network,
provided that the contributions from any closed loops in the network are sufficiently weak. In …

The canonical polyadic (CP) decomposition of tensors is one of the most important tensor
decompositions. While the well‐known alternating least squares (ALS) algorithm is often …

Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are
widely used structured models, both of which can be represented as factor graph grammars …

Graph neural network models have been extensively used to learn node representations for
graph structured data in an end-to-end setting. These models often rely on localized first …

We address the problem of Visual Relationship Detection (VRD) which aims to describe the
relationships between pairs of objects in the form of triplets of (subject, predicate, object). We …

Bayesian inference is a probabilistic approach to the problem of drawing conclusions from
observed data. Its main challenge is computational, which the Bayesian community tends to …

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of
which can learn powerful representations in an end-to-end fashion with great success in …

Given a graphical model (GM), computing its partition function is the most essential
inference task, but it is computationally intractable in general. To address the issue, iterative …