1. Ballas N, Yao L, Pal C, Courville A (2015) Delving deeper into convolutional networks for learning video representations. ArXiv preprint
arXiv:1511.06432
2. Binh HT, Duy BT (2017) Predicting students’ performance based on learning style by using artificial neural networks. In: Proceedings of the 9th International Conference on Knowledge and Systems Engineering (KSE), IEEE , pp 48–53
3. Boureau YL, Bach F, Lecun Y, Ponce J (2010) Learning mid-level features for recognition. In: Proceedings of the 27th computer vision and pattern recognition, IEEE, pp 2559–2566
4. Burgos C, Campanario ML, Peña D, Lara JA, Lizcano D, Martnez MA (2018) Data mining for modeling students performance: a tutoring action plan to prevent academic dropout. Comput Electr Eng 66:541–556
5. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv preprint
arXiv:1412.3555