The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Automated Machine Learning: Methods, Systems, Challenges
Open accessAbstract
This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge of AutoML systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
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
Cited by
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477
Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618
Meta-Learning in Neural Networks: A Survey
Hospedales, T., Antoniou, A., Micaelli, P., & Storkey, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05439 · Cited by 886
Meta-Learning in Neural Networks: A Survey
Hospedales, T. M., Antoniou, A., Micaelli, P., & Storkey, A. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3079209 · Cited by 886
Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., et al. (2020) · Computer · doi:10.1109/mc.2020.2996587 · Cited by 415
Ai for social good: unlocking the opportunity for positive impact
Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15871-z · Cited by 357
Perez-Cerrolaza, J., Abella, J., Borg, M., Donzella, C., Cerquides, J., Cazorla, F. J., Englund, C., Tauber, M., Nikolakopoulos, G., & Flores, J. L. (2024) · ACM Computing Surveys · doi:10.1145/3626314 · Cited by 185
Analysis of the Human Protein Atlas Image Classification competition
Ouyang, W., Winsnes, C. F., Hjelmare, M., Cesnik, A. J., Åkesson, L., Xu, H., Sullivan, D. P., Dai, S., Lan, J., Jinmo, P., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0658-6 · Cited by 131
Artificial intelligence predictive model for hormone therapy use in prostate cancer
Spratt, D. E., Tang, S., Sun, Y., Huang, H.-C., Chen, E., Mohamad, O., Armstrong, A. J., Tward, J. D., Nguyen, P. L., Lang, J. M., et al. (2023) · NEJM Evidence · doi:10.1056/evidoa2300023 · Cited by 153
A survey on optimization techniques for edge artificial intelligence (ai)
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023) · Sensors · doi:10.3390/s23031279 · Cited by 131
Adoption and Effects of Software Engineering Best Practices in Machine Learning
Serban, A., van der Blom, K., Hoos, H., & Visser, J. (2020) · Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) · doi:10.1145/3382494.3410681 · Cited by 127
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
Ruf, P., Madan, M., Reich, C., & Ould-Abdeslam, D. (2021) · Applied Sciences · doi:10.3390/app11198861 · Cited by 112
A review on the self and dual interactions between machine learning and optimisation
Song, H., Triguero, I., & Özcan, E. (2019) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00185-z · Cited by 101
A systematic review on machine learning models for online learning and examination systems
Kaddoura, S., Popescu, D. E., & Hemanth, J. D. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.986 · Cited by 75
Neural Architecture Search Benchmarks: Insights and Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3253818 · Cited by 64
Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study
Uchoa, A., Barbosa, C., Coutinho, D., Oizumi, W., Assuncao, W. K. G., Vergilio, S. R., Pereira, J. A., Oliveira, A., & Garcia, A. (2021) · 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00059 · Cited by 42
Promoting Fairness through Hyperparameter Optimization
F.Cruz, A., Saleiro, P., Belem, C., Soares, C., & Bizarro, P. (2021) · 2021 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm51629.2021.00119 · Cited by 28
Quality indices for topic model selection and evaluation: a literature review and case study
Meaney, C., Stukel, T. A., Austin, P. C., Moineddin, R., Greiver, M., & Escobar, M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02216-1 · Cited by 23
Designing optimal behavioral experiments using machine learning
Valentin, S., Kleinegesse, S., Bramley, N. R., Seriès, P., Gutmann, M. U., & Lucas, C. G. (2024) · eLife · doi:10.7554/elife.86224 · Cited by 8
Porębski, A. (2023) · Research Handbook on Law and Technology · doi:10.4337/9781803921327.00037 · Cited by 4
Demo Application for the AutoGOAL Framework
Estevez-Velarde, S., Piad-Morffis, A., Gutiérrez, Y., Montoyo, A., Muñoz-Guillena, R., & Almeida Cruz, Y. (2020) · Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.coling-demos.4
Metadata
- Venue
- The Springer Series on Challenges in Machine Learning
- Publisher
- Springer International Publishing
- Published
- 2019
- ISSN
- 2520-131X (ISSN-L)
- 2520-1328
- OpenAlex
- W4213308398
- Cited by
- 1,485
- References
- 42
- FWCI
- 65.87
- Type
- book
- Language
- en
- Open access
- hybrid
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
