Summary of Fully Tensorial Approach to Hypercomplex Neural Networks, by Agnieszka Niemczynowicz et al.
Fully tensorial approach to hypercomplex neural networks
by Agnieszka Niemczynowicz, Radosław Antoni Kycia
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 novel fully tensorial theory of hypercomplex neural networks is presented, enabling the utilization of arithmetic based on arbitrary algebras in neural networks. The core idea is to represent algebra multiplication as a rank-three tensor and apply this tensor to all algebraic operations. This approach has appeal for neural network libraries that support efficient tensor-based operations, building upon previous implementations for four-dimensional algebras. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of building artificial intelligence models is being explored. Usually, these models use simple addition and multiplication to make decisions. But what if we could use different types of math, like those used in physics or engineering? This could help the models be more powerful and flexible. The key idea is to represent complex mathematical operations as a special kind of data structure called a tensor. This allows the model to use different types of math, which can be useful for certain tasks. |
Keywords
» Artificial intelligence » Neural network
