Deep Learning Based Forecasting of Critical Infrastructure Data

By Zahra Zohrevand, Uwe Glässer, Mohammad A. Tayebi, Hamed Yaghoubi Shahir, Mehdi Shirmaleki, Amir Yaghoubi Shahir. 2017

18 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Daniel J. Hsu
2. Roxana Geambasu
3. Vaggelis Atlidakis
4. Suman Sekhar Jana
5. Alekh Agarwal
6. J. Langford

In Signal Processing

1. Daniel J. Hsu
2. Roxana Geambasu
3. Vaggelis Atlidakis
4. Mathias Lécuyer
5. Suman Sekhar Jana
6. Anima Anandkumar
7. Sham M. Kakade
8. Sylvain Calinon

Citations in the corpus

• Certified Robustness to Adversarial Examples with Differential Privacy (2018, background): “Deep neural networks (DNNs) perform exceptionally well on many artificial intelligence tasks, including safety- and security-sensitive applications such as selfdriving cars [5], malware classification [47], face recognition [46], and critical infrastructure systems [63].”
• On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning (2018, unknown): “nition [36], and critical infrastructure systems [47].”