Summary of Zorro: a Flexible and Differentiable Parametric Family Of Activation Functions That Extends Relu and Gelu, by Matias Roodschild et al.
Zorro: A Flexible and Differentiable Parametric Family of Activation Functions That Extends ReLU and GELU
by Matias Roodschild, Jorge Gotay-Sardiñas, Victor A. Jimenez, Adrian Will
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel set of activation functions called Zorro, designed for neural networks. Building upon existing approaches like ReLU, GELU, and Swish, Zorro offers a continuously differentiable and flexible family of five main functions. Unlike traditional alternatives, Zorro provides an alternative to ReLU without requiring normalization or neuron death. Tested on fully connected, convolutional, and transformer architectures, Zorro demonstrates its effectiveness in capturing nonlinear data patterns and enabling more efficient training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Zorro is a new way to make neural networks work better. Right now, we have many different ways to do this called activation functions. But some of them can cause problems like exploding gradients or neurons dying. Zorro solves these issues by being smooth and adaptable. It’s like an information gate that lets the right data through. Zorro is also good at imitating other popular activation functions like Swish, GELU, and DGELU. This makes it a useful tool for building neural networks. |
Keywords
» Artificial intelligence » Relu » Transformer
