Fractional-Order Optimization

Innovating neural networks through advanced fractional-order gradient descent algorithms and optimization techniques.

Innovative Fractional-Order Optimization Research

We specialize in developing fractional-order gradient descent algorithms and optimizing neural networks using advanced mathematical frameworks and OpenAI technologies for enhanced model performance.

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Fractional-Order Optimization

Specializing in fractional-order algorithms for neural networks and advanced optimization techniques for model training.

Algorithm Development

Constructing mathematical frameworks for fractional-order gradient descent and backpropagation algorithms tailored for neural networks.

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Implementation Testing

Implementing and testing fractional-order variants like SGD and Adam on benchmark datasets for performance evaluation.

Utilizing OpenAI API

API Integration

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Fractional Optimization

Exploring fractional-order algorithms for neural network optimization.

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Phase One

Constructing mathematical framework for fractional-order algorithms.

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Phase Two

Implementing and testing fractional-order optimizer variants.

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Phase Three

Applying optimizers to train and evaluate GPT models.

Evaluation

Systematic assessment of fractional-order parameters on performance.