The DOI matches its OpenAlex record; 5 of the 5 references it cites were found in a registry or an index.
Automatic differentiation in machine learning: a survey
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Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754
Graves, A., Wayne, G., & Danihelka, I. (2014) · arXiv · doi:10.48550/arxiv.1410.5401 · Cited by 1,058
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Seide, F., & Agarwal, A. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945397 · Cited by 382
Cited by
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
The frontier of simulation-based inference
Cranmer, K., Brehmer, J., & Louppe, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912789117 · Cited by 1,032
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 930
Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv · doi:10.48550/arxiv.1603.00788 · Cited by 515
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
Eivazi, H., Tahani, M., Schlatter, P., & Vinuesa, R. (2022) · Physics of Fluids · doi:10.1063/5.0095270 · Cited by 490
Metadata
- Authors
- Baydin, A. G., Pearlmutter, B. A., Radul, A., & Siskind, J. M.
- Venue
- arXiv (Cornell University)
- Publisher
- Cornell University
- Published
- 2015-02-20
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W1853291804
- Cited by
- 823
- References
- 5
- Type
- posted content
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Automatic differentiation in machine learning: a survey. https://app.cochat.ai/papers/10.4230/lipics.ecoop.2019.25
- The paper
- Baydin, A. G., Pearlmutter, B. A., Radul, A., & Siskind, J. M. (2015). Automatic differentiation in machine learning: a survey. arXiv (Cornell University). https://doi.org/10.4230/lipics.ecoop.2019.25
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.
