Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting

Area of Science:

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence

Background:

• Attractor neural networks (ANNs) are computational models inspired by biological neural networks.
• Traditional ANNs often face challenges with overfitting and generalization.
• A machine learning perspective offers novel approaches to optimize ANN parameters.

Purpose of the Study:

• To apply machine learning techniques, specifically gradient descent on a regularized loss function, to optimize attractor neural network parameters.
• To investigate the relationship between unlearning protocols, regularization, and training time in ANNs.
• To analyze the generalization capabilities of optimized ANNs across different data regimes.

Main Methods:

• Utilized gradient descent optimization on a regularized loss function to determine optimal network parameters.
• Identified optimal neuron-interaction matrices as Hebbian kernels modified by an unlearning protocol.
• Conducted analytical and numerical experiments on synthetic datasets to evaluate network performance and generalization.

Main Results:

• The extent of the unlearning protocol is directly related to the regularization hyperparameter and training duration.
• Developed strategies for overfitting avoidance through regularization and early-stopping.
• Identified distinct performance regimes (overfitting, failure, success) based on dataset parameters.

Conclusions:

• Hebbian kernels revised by unlearning offer an effective approach to training ANNs within a machine learning framework.
• Regularization and early stopping are crucial for managing overfitting and enhancing generalization in ANNs.
• The study provides a framework for designing and analyzing ANNs with improved predictive performance.