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Methodology

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TensorNetwork is an open source library for implementing tensor network algorithms.

TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow.

Tensor networks are approximations of high-order tensors which are efficient to work with and have been very successful for physics and mathematics applications.

Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications.

Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning.

This requires the detection of visual relationships: triples (subject, relation, object) describing a semantic relation between a subject and an object.

RELATIONAL REASONING TENSOR NETWORKS VISUAL RELATIONSHIP DETECTION ZERO-SHOT LEARNING

We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.

The interest in machine learning with tensor networks has been growing rapidly in recent years.

We study the quantum features of the TN states, including quantum entanglement and fidelity.

Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.