Summary of Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond, by Yusuke Matsui and Tatsuya Yokota
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond
by Yusuke Matsui, Tatsuya Yokota
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A new operator called the broadcast product is proposed for calculating the Hadamard product between two tensors with aligned shapes. This operator can simplify complex tensor operations in libraries like NumPy by allowing them to be represented as mathematical expressions. Furthermore, a novel tensor decomposition using the broadcast product is introduced, highlighting its potential applications in dimensionality reduction and other tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of combining two groups of numbers (tensors) is proposed. This new method, called the broadcast product, helps simplify complex calculations by allowing them to be written in a more straightforward mathematical form. The broadcast product also has the potential to help with reducing the number of dimensions in data, which can make it easier to understand and work with. |
Keywords
» Artificial intelligence » Dimensionality reduction
