Neural SDE-Based Epistemic Uncertainty Quantification in Deep Neural Networks

Author:

Tharzeen Aabila,Dahale Shweta,Natarajan Balasubramaniam

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297 (2021)

2. Amini, A., Liu, G., Motee, N.: Robust learning of recurrent neural networks in presence of exogenous noise. In: 2021 60th IEEE Conference on Decision and Control (CDC), pp. 783–788. IEEE (2021)

3. Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613–1622. PMLR (2015)

4. Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. Adv. Neural Inf. Process. Syst. 31 (2018)

5. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning [eb/ol]. arXiv preprint arxiv:1506.02142 (2015)

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