Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 26·5 min read
On the Epistemic Uncertainty of Overparametrized Neural Networks
TL;DR
Overparametrized neural networks, which have more parameters than necessary, can lead to epistemic uncertainty, meaning uncertainty in model predictions due to lack of knowledge. This study introduces a method to quantify this uncertainty using Bayesian techniques, specifically through the application of variational inference.
✦ Why It Matters
Engineers can enhance neural network reliability by integrating epistemic uncertainty quantification into their models.
Key Takeaways
- Understand the implications of parameter non-identifiability in your models.
- Reevaluate how you quantify uncertainty in neural network predictions.
- Consider the effects of model architecture on predictive variability.
- Incorporate findings into your training data strategies to mitigate uncertainty.
How It Works
The study explores the relationship between epistemic uncertainty and parameter identifiability in overparametrized neural networks. It shows that due to the complex structure of these networks, certain parameters cannot be uniquely determined, leading to ongoing uncertainty even when the model's function is clear.
By analyzing one-hidden-layer ReLU networks, the authors provide a framework for understanding how this uncertainty manifests in predictions.