Fractional Concepts in Neural Networks: Enhancing Activation and Loss Functions

• URL: http://arxiv.org/abs/2310.11875v1
• Date: Wed, 18 Oct 2023 10:49:29 GMT
• Title: Fractional Concepts in Neural Networks: Enhancing Activation and Loss Functions
• Authors: Zahra Alijani, Vojtech Molek
• Abstract summary: The paper presents a method for using fractional concepts in a neural network to modify the activation and loss functions. This will enable neurons in the network to adjust their activation functions to match input data better and reduce output errors.
• Score: 0.7614628596146602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper presents a method for using fractional concepts in a neural network to modify the activation and loss functions. The methodology allows the neural network to define and optimize its activation functions by determining the fractional derivative order of the training process as an additional hyperparameter. This will enable neurons in the network to adjust their activation functions to match input data better and reduce output errors, potentially improving the network's overall performance.

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