| year | 2021 |
| author(s) | Laura Kriener and Julian Göltz and Mihai A. Petrovici |
| title | The Yin-Yang dataset |
| KIP-Nummer | HD-KIP 21-12 |
| KIP-Gruppe(n) | F9 |
| document type | Paper |
| source | arXiv:2102.08211 |
| Abstract (en) | The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in algorithm- and model-prototyping scenarios, by providing several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for the deployment on neuromorphic chips with limited network sizes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks. |
| bibtex | @article{kriener2021yinyang,
author = {Laura Kriener and Julian Göltz and Mihai A. Petrovici},
title = {The Yin-Yang dataset},
journal = {arXiv},
year = {2021},
volume = {},
pages = {},
url = {https://arxiv.org/abs/2102.08211}
} |
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| URL | https://arxiv.org/abs/2102.08211 |
