Summary of Very Basics Of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositions, by Tatsuya Yokota
Very Basics of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositions
by Tatsuya Yokota
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces tensor network diagram (graphical notation) as a tool to simplify complex multiplications between multiple tensors. The graphical representation uses nodes and edges to describe operations like inner product, outer product, Hadamard product, Kronecker product, and Khatri-Rao product. This notation helps understand the essence of tensor products and is essential for matrix/tensor decompositions in signal processing and machine learning. The paper aims to provide a comprehensive introduction to tensors and their representation in mathematical symbols and graphical notation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tensor networks are a way to visualize complex calculations between multiple tensors. It’s like drawing a picture to show how different parts fit together. This helps make sense of things like matrix/tensor decompositions, which are important for signal processing and machine learning. The goal is to give readers a solid understanding of what tensors are and how they’re used. |
Keywords
» Artificial intelligence » Machine learning » Signal processing
