The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Neural Networks and Deep Learning: A Textbook
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Citations
Cites
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.01815 · Cited by 1,102 · cited 7×
“(c) Positions from two different games between Alpha Zero (white) and Stockfish (black) [447]: On the left, white sacrifices a pawn and concedes a passed pawn in order to trap black’s light-square bishop behind black’s own pawns.”
InfluentialEnergy-based Generative Adversarial Network
Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · cited 1×
“Energy-based GANs are proposed in [562], it is claimed that they have better stability.”
Cited by
Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044
The promises and challenges of artificial intelligence for teachers: A systematic review of research
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022) · TechTrends · doi:10.1007/s11528-022-00715-y · Cited by 984
Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654
Non-intrusive reduced order modeling of nonlinear problems using neural networks
Hesthaven, J. S., & Ubbiali, S. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.02.037 · Cited by 616
Metadata
- Authors
- Aggarwal, C. C.
- Publisher
- Springer International Publishing
- Published
- 2018
- OpenAlex
- W2888499542
- Cited by
- 417
- References
- 2
- FWCI
- 24.05
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- CoChat. (2026). Citation check: Neural Networks and Deep Learning: A Textbook. https://app.cochat.ai/papers/10.1007/978-3-319-94463-0
- The paper
- Aggarwal, C. C. (2018). Neural Networks and Deep Learning: A Textbook. https://doi.org/10.1007/978-3-319-94463-0
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.
