mlsanityData to 7 Oct 2026

TedNet: A Pytorch Toolkit for Tensor Decomposition Networks

arXiv 2104.05018 ↗First posted 11 Apr 2021cs.LG cs.AIcited by 9

Not on the map. Why?

Abstract (arXiv)

Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named TedNet. TedNet implements 5 kinds of tensor decomposition(i.e., CANDECOMP/PARAFAC (CP), Block-Term Tucker (BTT), Tucker-2, Tensor Train (TT) and Tensor Ring (TR) on traditional deep neural layers, the convolutional layer and the fully-connected layer. By utilizing the basic layers, it is simple to construct a variety of TDNs. TedNet is available at https://github.com/tnbar/tednet.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 11 Apr 2021.

Builds on

12 of 23 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  4. 2020Human Action Recognition from Various Data Modalities: A Reviewcited by 162
  5. 2016TensorLy: Tensor Learning in Pythoncited by 104
  6. 2017Tensor-Train Recurrent Neural Networks for Video Classificationcited by 59
  7. 2018Compressing Recurrent Neural Networks with Tensor Ring for Action Recognitioncited by 34
  8. 2017Learning Compact Recurrent Neural Networks with Block-Term Tensor Decompositioncited by 33
  9. 2020Heuristic Rank Selection with Progressively Searching Tensor Ring Networkcited by 28
  10. 2019TensorNetwork: A Library for Physics and Machine Learningcited by 21
  11. 2020Block-term Tensor Neural Networkscited by 13
  12. 2021On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Methodcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

9 papers.

  1. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 29
  2. 2023Reusing Pretrained Models by Multi-linear Operators for Efficient Trainingcited by 15
  3. 2022A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networkscited by 8
  4. 2022tntorch: Tensor Network Learning with PyTorchcited by 7
  5. 2024conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networkscited by 1
  6. 2023Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximationcited by 0
  7. 2023Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Frameworkcited by 0
  8. 2024Reduced storage direct tensor ring decomposition for convolutional neural networks compressioncited by 0
  9. 2026Quantum-inspired tensor networks in machine learning modelscited by 0