Unveiling Neural Network Generalization Mechanisms

The field of deep learning is witnessing significant advancements in understanding neural network generalization. Recent studies have been shedding light on the underlying mechanisms that drive neural networks to generalize well. A notable direction of research focuses on the phenomenon of grokking, where neural networks abruptly generalize after reaching a near-perfect training performance. This has led to a deeper understanding of the computational processes involved in neural network learning. Furthermore, novel optimization algorithms inspired by physical systems and kinetic theory are being developed, which mitigate issues like parameter condensation and promote diversity during optimization. Additionally, insights into the role of embeddings in grokking and the development of more expressive Lipschitz neural architectures are contributing to a better understanding of neural network generalization. Noteworthy papers in this area include:

  • The paper proposing a computational glass relaxation interpretation for grokking, which challenges previous theories and introduces a novel optimizer.
  • The work introducing a kinetics-inspired neural optimizer that promotes parameter diversity and outperforms baseline optimizers.

Sources

Is Grokking a Computational Glass Relaxation?

New Evidence of the Two-Phase Learning Dynamics of Neural Networks

Bridging Predictive Coding and MDL: A Two-Part Code Framework for Deep Learning

KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches

Generalization Through Growth: Hidden Dynamics Controls Depth Dependence

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss

Mechanistic Insights into Grokking from the Embedding Layer

Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks

Stochastic Forward-Forward Learning through Representational Dimensionality Compression

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