Neural network structure simplification by assessing evolution in node weight magnitude
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06438-2.pdf
Reference44 articles.
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3. Cai, L., An, Z., Yang, C., & Xu, Y. (2021). Softer pruning, incremental regularization. In 2020 25th international conference on pattern recognition (ICPR), pp. 224–230. https://doi.org/10.1109/ICPR48806.2021.9412993
4. Choudhary, T., Mishra, V., Goswami, A., & Sarangapani, J. (2021). A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices. Computers in Biology and Medicine, 134, 104432. https://doi.org/10.1016/j.compbiomed.2021.104432
5. Chouliaras, A., Fragkou, E., & Katsaros, D. (2022). Feed forward neural network sparsification with dynamic pruning. In Proceedings of the 25th pan-hellenic conference on informatics. PCI ’21, pp. 12–17. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3503823.3503826. Accessed 2023–05–21.
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