KHNNs: hypercomplex neural networks computations via Keras using TensorFlow and PyTorch
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Abstract (arXiv)
Neural networks used in computations with more advanced algebras than real numbers perform better in some applications. However, there is no general framework for constructing hypercomplex neural networks. We propose a library integrated with Keras that can do computations within TensorFlow and PyTorch. It provides Dense and Convolutional 1D, 2D, and 3D layers architectures.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 46 references that are in the map. First posted on arXiv 29 Jun 2024.
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References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2017Deep Quaternion Networkscited by 48
- 2018Quaternion Convolutional Neural Networks for Heterogeneous Image Processingcited by 29
- 2021PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutionscited by 26
- 2021Quaternion Generative Adversarial Networkscited by 17
- 2019Deep Octonion Networkscited by 8
- 2024Hypercomplex neural network in time series forecasting of stock datacited by 2
- 2018Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognitioncited by 1
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