Summary of Stable and Robust Deep Learning by Hyperbolic Tangent Exponential Linear Unit (telu), By Alfredo Fernandez and Ankur Mali
Stable and Robust Deep Learning By Hyperbolic Tangent Exponential Linear Unit (TeLU)
by Alfredo Fernandez, Ankur Mali
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel neural network activation function called Hyperbolic Tangent Exponential Linear Unit (TeLU) is introduced, which addresses the limitations of conventional activation functions by stabilizing gradient updates. TeLU is designed to reduce vanishing and exploding gradients, leading to enhanced training stability and convergence. Compared to popular activation functions like ReLU, GELU, SiLU, Mish, Logish, and Smish, TeLU exhibits lower variance and superior performance in various deep learning applications, including Resnet-50 architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TeLU is a new way of making neural networks work better. It’s an activation function that helps train the network by preventing some problems with gradients getting too big or too small. This makes the training process more stable and faster to complete. TeLU was tested on many different types of networks and datasets, including pictures of animals and objects, and it performed well in all scenarios. |
Keywords
* Artificial intelligence * Deep learning * Neural network * Relu * Resnet
