Tensor Networks
52 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Tensor Networks models and implementationsMost 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
logictensornetworks/logictensornetworks
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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
ivanDonadello/Visual-Relationship-Detection-LTN
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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.
Tensor Networks for Medical Image Classification
raghavian/loTeNet_pytorch
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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
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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?
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.
