Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias
arXiv:2605.27097v1 Announce Type: new Abstract: The successful training of neural networks hinges on the use of first order optimization methods, yet the theoretical characterization of these methods remains incomplete. This is especially true in settings with mild overparameterization. In this work, we study the gradient flow dynamics of two-layer ReLU networks from small initialization with orthogonal training data. We prove the limiting flow converges to a saddle-to-saddle jump process as the initialization scale tends to zero, revealing an incremental learning phenomenon in which a new neu
This paper represents continued academic progress in understanding the fundamental training dynamics of neural networks, a crucial step for advancing AI capabilities.
A deeper theoretical understanding of neural network training, especially in overparameterized regimes, can lead to more efficient and robust AI systems, impacting future AI development.
This research contributes to the theoretical foundation of AI, potentially leading to more deliberate and optimized network designs and training methodologies rather than empirical trial-and-error.
- · AI researchers
- · AI model developers
- · Deep learning framework creators
Improved theoretical understanding of neural network learning processes.
Development of more efficient and predictable AI training algorithms and architectures.
Acceleration of advanced AI capabilities due to foundational breakthroughs in learning dynamics.
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