Strong mixed-integer programming formulations for trained neural networks

Author:

Anderson Ross,Huchette JoeyORCID,Ma WillORCID,Tjandraatmadja Christian,Vielma Juan Pablo

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics,Software

Reference83 articles.

1. https://developers.google.com/machine-learning/glossary/#logits. Accessed 6 Feb 2020

2. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015)

3. Amos, B., Xu, L., Kolter, J.Z.: Input convex neural networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 146–155. PMLR, International Convention Centre, Sydney (2017)

4. Anderson, R., Huchette, J., Tjandraatmadja, C., Vielma, J.P.: Strong mixed-integer programming formulations for trained neural networks. In: A. Lodi, V. Nagarajan (eds.) Proceedings of the 20th Conference on Integer Programming and Combinatorial Optimization, pp. 27–42. Springer International Publishing, Cham (2019). arxiv:1811.08359

5. Arora, R., Basu, A., Mianjy, P., Mukherjee, A.: Understanding deep neural networks with rectified linear units (2016). arXiv preprint arXiv:1611.01491

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