Training Overparametrized Neural Networks: Early Alignment Phenomenon and Simplicity Bias

Overview

Delve into the early alignment phase in neural network training dynamics, exploring how neurons align towards key directions, creating sparsity that can lead to better generalization despite potential local minima.

Syllabus

- Introduction to Neural Networks -- Overview of neural network architectures -- Understanding parameters and overparameterization - Early Alignment Phase -- Definition and importance -- Mathematical foundations -- Empirical evidence from recent studies - Dynamics of Neuron Alignment -- Key directions and feature selection -- Role of gradients in early alignment -- Case studies of effective neuron alignment - Sparsity in Neural Networks -- Definition and measurement of sparsity -- Connection between early alignment and sparsity -- Benefits of sparsity for neural network generalization - Simplicity Bias in Training -- Understanding complexity versus simplicity -- How early alignment induces simplicity -- Simplicity bias effects on generalization - Training Dynamics and Local Minima -- Exploration of local versus global minima -- Early alignment as a mechanism to avoid poor minima -- Practical techniques for navigating training landscapes - Practical Implications -- Designing neural networks with alignment and sparsity in mind -- Real-world applications and case studies -- Tools and libraries for implementing the concepts - Advanced Topics and Current Research -- Recent advancements in understanding early alignment -- Open research questions and future directions - Conclusion and Further Reading -- Recap of key concepts -- Suggested literature for deeper exploration

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