Summary of Khnns: Hypercomplex Neural Networks Computations Via Keras Using Tensorflow and Pytorch, by Agnieszka Niemczynowicz et al.
KHNNs: hypercomplex neural networks computations via Keras using TensorFlow and PyTorch
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 This paper proposes a new library that enables the construction of hypercomplex neural networks for computations using advanced algebras like quaternions or octonions. The authors provide a framework integrated with popular deep learning frameworks like Keras, TensorFlow, and PyTorch, allowing users to build complex architectures such as Dense and Convolutional 1D, 2D, and 3D layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making special kinds of artificial intelligence called neural networks work better in some situations. Right now, there’s no good way to make these networks use math with quaternions or octonions. The authors created a new tool that lets people build these advanced networks using popular tools like Keras and TensorFlow. |
Keywords
* Artificial intelligence * Deep learning
