Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks
📰 ArXiv cs.AI
New research explores initialization-dependent generalization bounds for overparameterized shallow neural networks
Action Steps
- Analyze the relationship between generalization and the norm of distance from initialization
- Investigate the role of initialization in benign overfitting
- Develop new generalization bounds that take into account the initialization-dependent properties of overparameterized neural networks
- Apply these bounds to improve the design and training of neural networks
Who Needs to Know This
ML researchers and AI engineers benefit from this research as it provides new insights into the generalization behavior of overparameterized neural networks, which can inform the design of more efficient and effective models
Key Insight
💡 The distance from initialization is a key factor in determining the generalization behavior of overparameterized neural networks
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Key Takeaways
New research explores initialization-dependent generalization bounds for overparameterized shallow neural networks
Full Article
Title: Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks
Abstract:
arXiv:2604.00505v1 Announce Type: cross Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the number of parameters exceeding the number of training examples. A promising direction to explain benign overfitting is to relate generalization to the norm of distance from initialization, motivated by the empirical observations that this distance is often significantly smaller than the norm itself. Ho
Abstract:
arXiv:2604.00505v1 Announce Type: cross Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the number of parameters exceeding the number of training examples. A promising direction to explain benign overfitting is to relate generalization to the norm of distance from initialization, motivated by the empirical observations that this distance is often significantly smaller than the norm itself. Ho
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