Is the Last Layer Sufficient for Uncertainty Quantification?
A new research paper explores the effectiveness of using only the last layer of a deep neural network for uncertainty quantification. The study found that this simplified approach, known as last-layer linearization, provides comparable performance to full-network linearization in modeling epistemic uncertainty. This method offers significant computational efficiency improvements, making it a viable option for safe AI deployment in critical applications. AI
IMPACT This research could enable more efficient and safer deployment of AI in critical systems by simplifying uncertainty quantification methods.