DIRECTED RELU NEURAL NETWORK MODEL AND ITS APPLICATION
Keywords:
Directed ReLU, activation function, deep learning, neural networks, machine learning, computational efficiency, artificial intelligence.Abstract
The Rectified Linear Unit (ReLU) is one of the most widely used activation functions in deep learning, known for its simplicity and effectiveness in training neural networks. The Directed ReLU (DReLU) model extends this function by introducing directional dependencies, enhancing the model's capacity to capture complex relationships in data. This paper explores the theoretical foundations of the Directed ReLU Neural Network Model, its computational benefits, and its applications in fields such as computer vision, natural language processing, and scientific computing. By reviewing existing literature and conducting empirical evaluations, the study demonstrates how the DReLU model improves learning efficiency and predictive accuracy. The paper also discusses challenges and potential future developments in directed activation functions.
References
1. Glorot, X., & Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the International Conference on Artificial Intelligence and Statistics.
2. Nair, V., & Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. ICML.
3. Maas, A. L., et al. (2013). Rectifier Nonlinearities Improve Neural Network Acoustic Models. ICML.
4. He, K., et al. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. IEEE ICCV.
5. Ramachandran, P., et al. (2017). Searching for Activation Functions. arXiv:1710.05941.
6. Goodfellow, I., et al. (2016). Deep Learning. MIT Press.
7. Zeiler, M. D., & Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. ECCV.
8. Krizhevsky, A., et al. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS.
9. Vaswani, A., et al. (2017). Attention is All You Need. NeurIPS.
10. Radford, A., et al. (2021). Learning Transferable Visual Models from Natural Language Supervision. ICML.
11. Lecun, Y., et al. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE.
12. Bengio, Y., et al. (2009). Learning Deep Architectures for AI. Foundations and Trends in Machine Learning.
13. Huang, G., et al. (2017). Densely Connected Convolutional Networks. CVPR.
14. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. ICLR.
15. Srivastava, N., et al. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. JMLR.
16. Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML.
17. Jastrzębski, S., et al. (2017). Three Factors Influencing Minima in SGD. NeurIPS.
18. Hanin, B., & Rolnick, D. (2018). How to Start Training: The Effect of Initialization and Architecture. NeurIPS.
