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One Sentence Abstract

The Gaussian Error Linear Unit (GELU) activation function, which weighs inputs by value and uses the standard Gaussian cumulative distribution function, outperforms ReLU and ELU functions across computer vision, natural language processing, and speech tasks.

Simplified Abstract

This research introduces a new method called the Gaussian Error Linear Unit (GELU) for neural networks. Think of a neural network as a tool that helps computers understand information, and an activation function as a step in that process that determines how the network processes information.

Traditional activation functions include ReLU and ELU. ReLU "switches on" when an input is positive and ELU when it's negative. GELU, on the other hand, "weights" inputs based on their value, like selecting the most important items in a list. It uses the standard Gaussian cumulative distribution function for this, which is a mathematical equation that describes how a bell-shaped curve is formed.

To test the effectiveness of GELU, the researchers compared its performance to ReLU and ELU in various tasks, such as analyzing images (computer vision), understanding written text (natural language processing), and interpreting speech. The results showed that GELU outperformed the other methods, making it a more accurate and reliable tool for these tasks.

In summary, this study introduces the GELU method, which improves the performance of neural networks in various applications. This innovation offers a new, more effective tool for scientists collaborating across countries to work with and improve their results.

Study Fields

Main fields:

• Neural Networks
• Activation Functions

Subfields:

• Gaussian Error Linear Unit (GELU)
• Standard Gaussian Cumulative Distribution Function (Φ(x))
• ReLU (Rectified Linear Unit)
• Empirical Evaluation
• Computer Vision Tasks
• Natural Language Processing Tasks
• Speech Tasks

Study Objectives

• Develop a high-performing neural network activation function called Gaussian Error Linear Unit (GELU)
• Compare the performance of GELU, ReLU, and ELU activations in computer vision, natural language processing, and speech tasks
• Demonstrate the improvement of GELU nonlinearity in empirical evaluations over ReLU and ELU activations

Conclusions

• The Gaussian Error Linear Unit (GELU) is a high-performing neural network activation function that improves upon the ReLU and ELU activations.
• GELU weights inputs by their value, whereas ReLU gates inputs by their sign. The GELU function, defined as xΦ(x), uses the standard Gaussian cumulative distribution function, Φ(x), to achieve this.
• The study demonstrates that GELU outperforms ReLU and ELU across various computer vision, natural language processing, and speech tasks.
• The empirical evaluation suggests that GELU is a promising activation function for neural networks, offering potential improvements in a range of applications.

References