1. Bojarski M, Testa DD, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J, Zhang X, Zhao J, Zieba K (2016) End to end learning for self-driving cars. CoRR abs/1604.07316
2. Scheiner N, Appenrodt N, Dickmann J, Sick B (2019) Radar-based road user classification and novelty detection with recurrent neural network ensembles. In: 2019 IEEE Intelligent Vehicles Symposium, IV 2019, Paris, France, pp 722–729. https://doi.org/10.1109/IVS.2019.8813773
3. Zhang H, Weng T, Chen P, Hsieh C, Daniel L (2018) Efficient neural network robustness certification with general activation functions. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems, NeurIPS 2018, Montréal, Canada, pp 4944–4953. https://proceedings.neurips.cc/paper/2018/hash/d04863f100d59b3eb688a11f95b0ae60-Abstract.html
4. Lipton ZC, Kale DC, Elkan C, Wetzel RC (2016) Learning to diagnose with LSTM recurrent neural networks. In: Bengio, Y, LeCun, Y (eds) 4th International Conference Track Proceedings on Learning Representations, ICLR 2016, San Juan, Puerto Rico. http://arxiv.org/abs/1511.03677
5. Zhou Z, Liu G (2022) RoMFAC: a robust mean-field actor-critic reinforcement learning against adversarial perturbations on states. CoRR abs/2205.07229 https://doi.org/10.48550/ARXIV.2205.07229 2205.07229