Tensor Networks
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TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks
TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow.
TensorNetwork: A Library for Physics and Machine Learning
TensorNetwork is an open source library for implementing tensor network algorithms.
Supervised Learning with Tensor Networks
Tensor networks are approximations of high-order tensors which are efficient to work with and have been very successful for physics and mathematics applications.
Supervised Learning with Quantum-Inspired Tensor Networks
Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications.
Can recursive neural tensor networks learn logical reasoning?
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.
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
• logictensornetworks/logictensornetworks
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This requires the detection of visual relationships: triples (subject, relation, object) describing a semantic relation between a subject and an object.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
• logictensornetworks/logictensornetworks
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We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Tensor-based algorithms for image classification
The interest in machine learning with tensor networks has been growing rapidly in recent years.
Expressive power of tensor-network factorizations for probabilistic modeling
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.