| _version_ | 1866901254649675776 |
|---|---|
| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | This paper explores the crucial role of activation functions in neural networks, moving beyond the limitations of traditional rectified linear units (ReLU) and sigmoid functions. We propose a reassessment of activation function geometry, focusing on novel designs that enhance network performance through improved gradient flow, increased expressiveness, and better handling of vanishing/exploding gradients. Our investigation encompasses a comprehensive analysis of various activation function properties, including smoothness, monotonicity, and boundedness, and their impact on network training dynamics. We introduce and evaluate several new activation function architectures, demonstrating their superior performance on benchmark datasets compared to standard activation functions. Furthermore, we provide theoretical insights into the relationship between activation function geometry and network optimization, shedding light on the conditions necessary for stable and efficient training. Our findings offer a fresh perspective on activation function design, paving the way for more powerful and robust neural network models. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17829591 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Beyond Rectification: Reassessing Activation Function Geometry for Enhanced Neural Network Performance Revista, Zen IA, 10 This paper explores the crucial role of activation functions in neural networks, moving beyond the limitations of traditional rectified linear units (ReLU) and sigmoid functions. We propose a reassessment of activation function geometry, focusing on novel designs that enhance network performance through improved gradient flow, increased expressiveness, and better handling of vanishing/exploding gradients. Our investigation encompasses a comprehensive analysis of various activation function properties, including smoothness, monotonicity, and boundedness, and their impact on network training dynamics. We introduce and evaluate several new activation function architectures, demonstrating their superior performance on benchmark datasets compared to standard activation functions. Furthermore, we provide theoretical insights into the relationship between activation function geometry and network optimization, shedding light on the conditions necessary for stable and efficient training. Our findings offer a fresh perspective on activation function design, paving the way for more powerful and robust neural network models. |
| title | Beyond Rectification: Reassessing Activation Function Geometry for Enhanced Neural Network Performance |
| url | https://doi.org/10.5281/zenodo.17829591 |