Author:
Konstantinov A. V.,Utkin L. V.
Reference17 articles.
1. P. Marquez-Neila, M. Salzmann, and P. Fua, “Imposing hard constraints on deep networks: Promises and Limitations,” in CVPR Workshop on Negative Results in Computer Vision (2017), pp. 1–9.
2. T. Frerix, M. Niessner, and D. Cremers, “Homogeneous linear inequality constraints for neural network activations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020), pp. 748–749.
3. J. Y. Lee, S. V. Mehta, M. Wick, J.-B. Tristan, and J. Carbonell, “Gradient-based inference for networks with output constraints,” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19) (2019), Vol. 33, pp. 4147–4154.
4. P. L. Donti, D. Rolnick, and J. Z. Kolter, “DC3: A learning method for optimization with hard constraints,” International Conference on Learning Representations (ICLR) (2021), pp. 1–17.
5. M. Brosowsky, F. Keck, O. Dunkel, and M. Zollner, “Sample-specific output constraints for neural networks,” The 35th AAAI Conference on Artificial Intelligence (AAAI-21) (2021), pp. 6812–6821.
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