Understanding AI Better with Brain Research
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Publisher
Springer Berlin Heidelberg
Link
https://link.springer.com/content/pdf/10.1007/978-3-662-68980-6_22
Reference16 articles.
1. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE, 10(7), e0130140.
2. Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6541–6549).
3. Binder, A., Bach, S., Montavon, G., Müller, K. R., & Samek, W. (2016a). Layer-wise relevance propagation for deep neural network architectures. In Information science and applications (ICISA) 2016 (pp. 913–922). Springer Singapore.
4. Binder, A., Montavon, G., Lapuschkin, S., Müller, K. R., & Samek, W. (2016b). Layer-wise relevance propagation for neural networks with local renormalization layers. In Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6–9, 2016, Proceedings, Part II 25 (pp. 63–71). Springer International Publishing.
5. Krauss, P., Metzner, C., Schilling, A., Tziridis, K., Traxdorf, M., Wollbrink, A., …, & Schulze, H. (2018a). A statistical method for analyzing and comparing spatiotemporal cortical activation patterns. Scientific Reports, 8(1), 5433.
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