Residual Recurrent Highway Networks for Learning Deep Sequence Prediction Models

By Tehseen Zia, Saad Razzaq. 2018

23 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Model Architectures, 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 Artificial Intelligence

1. Boris N. Oreshkin
2. Nicolas Chapados
3. Yoshua Bengio
4. Han-Jia Ye
5. Kyunghyun Cho
6. Dacheng Tao
7. Sergey Levine
8. De-chuan Zhan

In Model Architectures

1. Boris N. Oreshkin
2. Dmitri Carpov
3. Yoshua Bengio
4. Nicolas Chapados
5. Kyunghyun Cho
6. Aaron C. Courville
7. Quoc V. Le
8. J. Urgen Schmidhuber

In Time-Series & Forecasting

1. Dmitri Carpov
2. Yoshua Bengio
3. Nicolas Chapados
4. Boris N. Oreshkin
5. Haixu Wu
6. Mingsheng Long
7. Jianmin Wang
8. Jan Gasthaus

Citations in the corpus

• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, background): “Somewhat independently, the work in the modern deep learning TS forecasting developed based on variations of recurrent neural networks [8, 27, 29, 32] being largely dominated by the electricity load forecasting in the multivariate setup.”