Game Theory Meets Statistical Physics: A Novel Deep Neural Networks Design

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Physics

Background:

• Deep learning models often struggle with complex feature extraction and pattern classification.
• Integrating principles from game theory and statistical physics offers a novel approach to enhance learning frameworks.
• Existing methods may face scalability challenges with increasing network complexity.

Purpose of the Study:

• To develop a unified deep graphical representation integrating game theory and statistical physics for feature extraction and pattern classification.
• To enhance the performance and scalability of deep learning models through a novel neuron evaluation and filtering mechanism.
• To introduce a new model regularization technique based on Shapley values.

Main Methods:

• Neurons are modeled as players in a game-theory framework and particles in statistical physics.
• The feed-forward process is interpreted as a sequential game, with neuron contributions quantified using Shapley values.
• Monte-Carlo sampling is employed to approximate Shapley values, reducing computational complexity and improving scalability.

Main Results:

• The proposed framework enables effective feature extraction and pattern classification.
• Neurons are iteratively evaluated and filtered based on their contribution to a payoff function.
• The approach demonstrated superior performance in facial age estimation and gender classification tasks compared to existing models.

Conclusions:

• The integration of game theory and statistical physics provides a powerful unified learning framework.
• The Shapley value-based regularization and Monte-Carlo approximation enhance model performance and scalability.
• This novel approach offers significant improvements in accuracy, precision, recall, and F1-score for classification tasks.