# Tensor Networks

59 papers with code • 0 benchmarks • 0 datasets

## Benchmarks

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## Libraries

Use these libraries to find Tensor Networks models and implementations## Most implemented papers

# 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.

# Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure

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

# TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks

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

# Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge

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

# Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

We propose extensions for the Dynamic Memory Network (DMN), specifically within the attention mechanism, we call the resulting Neural Architecture as Dynamic Memory Tensor Network (DMTN).

# TensorNetwork: A Library for Physics and Machine Learning

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

# Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation

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

# Tensor Networks for Medical Image Classification

With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are coming to light.

# Faster-LTN: a neuro-symbolic, end-to-end object detection architecture

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation.

# Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection

Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).