Tensor Networks

52 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find Tensor Networks models and implementations

Most implemented papers

Supervised Learning with Quantum-Inspired Tensor Networks

emstoudenmire/TNML 18 May 2016

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

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

google/TensorNetwork 3 May 2019

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

logictensornetworks/logictensornetworks 14 Jun 2016

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

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

rgsachin/DMTN 11 Mar 2017

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

google/TensorNetwork 3 May 2019

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

Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation

ivanDonadello/Visual-Relationship-Detection-LTN 1 Oct 2019

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

raghavian/loTeNet_pytorch MIDL 2019

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.

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

pavliclab/aaai2022-clear2022-visual_relationship_detection-rwfn AAAI Workshop CLeaR 2022

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).

Can recursive neural tensor networks learn logical reasoning?

sleepinyourhat/vector-entailment 21 Dec 2013

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.