The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
Open accessAbstract
In many machine learning applications, labeled data is scarce and obtaining more labels is expensive. We introduce a new approach to supervising neural networks by specifying constraints that should hold over the output space, rather than direct examples of input-output pairs. These constraints are derived from prior domain knowledge, e.g., from known laws of physics. We demonstrate the effectiveness of this approach on real world and simulated computer vision tasks. We are able to train a convolutional neural network to detect and track objects without any labeled examples. Our approach can significantly reduce the need for labeled training data, but introduces new challenges for encoding prior knowledge into appropriate loss functions.
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Citations
Cites
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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
Richardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
Cited by
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
“Some pre-knowledge about the activity will greatly contribute to more robust feature learning in deep models (Stewart and Ermon, 2017).”
BackgroundDeep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
“For example, [53] detects and tracks objects in videos in an unsupervised way.”
BackgroundLIMITATIONS OF PHYSICS INFORMED MACHINE LEARNING FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA
Fuks, O., & Tchelepi, H. A. (2020) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2020033905 · Cited by 303
Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243
Metadata
- Authors
- Stewart, R., & Ermon, S.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-13
- Volume
- 31
- Issue
- 1
- ISSN
- 2374-3468
- 2159-5399
- OpenAlex
- W2963019788
- Cited by
- 274
- References
- 7
- FWCI
- 21.98
- Type
- journal article
- Language
- en
- Open access
- green
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