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Convergence Results for Neural Networks via Electrodynamics
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Cited by
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
Metadata
- Authors
- Panigrahy, R., Rahimi, A., Sachdeva, S., & Zhang, Q.
- Venue
- Leibniz international proceedings in informatics
- Published
- 2018-01-01
- ISSN
- 1868-8969 (ISSN-L)
- OpenAlex
- W2963285578
- Cited by
- 2,761
- References
- 0
- FWCI
- 153.97
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- CoChat. (2026). Citation check: Convergence Results for Neural Networks via Electrodynamics. https://app.cochat.ai/papers/10.4230/lipics.itcs.2018.22
- The paper
- Panigrahy, R., Rahimi, A., Sachdeva, S., & Zhang, Q. (2018). Convergence Results for Neural Networks via Electrodynamics. Leibniz international proceedings in informatics. https://doi.org/10.4230/lipics.itcs.2018.22
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
