Verifying Low-Dimensional Input Neural Networks via Input Quantization

Author:

Jia Kai,Rinard Martin

Publisher

Springer International Publishing

Reference20 articles.

1. Bak, S., Tran, H.D., Hobbs, K., Johnson, T.T.: Improved geometric path enumeration for verifying relu neural networks. In: International Conference on Computer Aided Verification, pp. 66–96, Springer (2020)

2. Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, Curran Associates, Inc. (2018). https://proceedings.neurips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf

3. Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M.W., Keutzer, K.: A survey of quantization methods for efficient neural network inference. arXiv preprint arXiv:2103.13630 (2021)

4. Jia, K., Rinard, M.: Efficient exact verification of binarized neural networks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1782–1795, Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/1385974ed5904a438616ff7bdb3f7439-Paper.pdf

5. Jia, K., Rinard, M.: Exploiting verified neural networks via floating point numerical error. arXiv preprint arXiv:2003.03021 (2020)

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