Towards Energy-Efficient Sentiment Classification with Spiking Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44204-9_43
Reference19 articles.
1. Singh, A., Pathak, K.: Sentiment analysis through Fourier transform techniques in NLP. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds.) Third Congress on Intelligent Systems. CIS 2022. LNNS, vol. 608, pp, 505–514. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-9225-4_37
2. Trisna, K.W., Jie, H.J.: Deep learning approach for aspect-based sentiment classification: a comparative review. Appl. Artif. Intell. 36(1), 2014186 (2022)
3. Pfeiffer, M., Pfeil, T.: Deep learning with spiking neurons: opportunities and challenges. Front. Neurosci. 12, 774 (2018)
4. Jin, X., et al.: RC-Darts: resource constrained differentiable architecture search. arXiv preprint arXiv:1912.12814 (2019)
5. Rueckauer, B., Lungu, I.-A., Yuhuang, H., Pfeiffer, M., Liu, S.-C.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017)
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