References
- D. Lee, "Comparison of Reinforcement Learning Activation Functions to Maximize Rewards in Autonomous Highway Driving," The Journal of the Institute of Internet, Broadcasting and Communication (JIIBC), Vol. 22, No. 5, pp. 63-68, 2022. DOI: https://doi.org/10.7236/JIIBC.2022.22.5.63
- A. Alkhouly, A. Mohammed, and H. Hefny, "Improving the Performance of Deep Neural Networks Using Two Proposed Activation Functions," IEEE Access, Vol. 9, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3085855
- Z. Hu, J. Zhang, and Y. Ge, "Handling Vanishing Gradient Problem Using Artificial Derivative," IEEE Access, Vol. 9, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3054915
- S. Douglas and J. Yu, "Why RELU Units Sometimes Die: Analysis of Single-Unit Error Backpropagation in Neural Networks," 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018. DOI: https://doi.org/10.1109/ACSSC.2018.8645556
- H. Zheng, Z. Yang, W. Liu, J. Liang and Y. Li, "Improving deep neural networks using softplus units," 2015 International Joint Conference on Neural Networks (IJCNN), 2015. DOI: https://doi.org/10.1109/IJCNN.2015.7280459
- J. Xu, Z. Li, B. Du, M. Zhang and J. Liu, "Reluplex made more practical: Leaky ReLU," 2020 IEEE Symposium on Computers and Communications (ISCC), 2020. DOI: https://doi.org/10.1109/ISCC50000.2020.9219587
- Y. Tsai, Y. Jheng and R. Tsaih, "The Cramming, Softening and Integrating Learning Algorithm with Parametric ReLU Activation Function for Binary Input/Output Problems," 2019 International Joint Conference on Neural Networks (IJCNN), 2019. DOI: https://doi.org/10.1109/IJCNN.2019.8852023
- J. Kang et al., "An Improved 3D Human Pose Estimation Model Based on Temporal Convolution with Gaussian Error Linear Units," 2022 8th International Conference on Virtual Reality (ICVR), 2022. DOI: https://doi.org/10.1109/ICVR55215.2022.9848068
- Z. Huang, T. Ng, L. Liu, H. Mason, X. Zhuang and D. Liu, "SNDCNN: Self-Normalizing Deep CNNs with Scaled Exponential Linear Units for Speech Recognition," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. DOI: https://doi.org/10.1109/ICASSP40776.2020.9053973
- J. Barron, "Continuously Differentiable Exponential Linear Units," arXiv preprint arXiv:1704.07483, 2017. DOI: https://doi.org/10.48550/arXiv.1704.07483
- S. Elfwing, E. Uchibe, and K. Doya, "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning," Neural Networks, Vol. 107, 2018. DOI: https://doi.org/10.1016/j.neunet.2017.12.012
- D. Misra, "Mish: A self regularized non-monotonic activation function," arXiv preprint arXiv:1908.08681, 2019. DOI: https://doi.org/10.48550/arXiv.1908.08681
- E. Leurent, "An Environment for Autonomous Driving Decision-Making," GitHub repository: https://github.com/eleurent/highway-env, 2018.
- J. Schulman, et al., "Proximal policy optimization algorithms," arXiv preprint arXiv:1707.06347, 2017. DOI: https://doi.org/10.48550/arXiv.1707.06347
- C. Garbin, Z. Xingquan, and O. Marques, "Dropout vs. batch normalization: an empirical study of their impact to deep learning," Multimedia Tools and Applications, Vol. 79, 2020. DOI: https://doi.org/10.1007/s11042-019-08453-9