Through considerable effort and intuition, several recent works have reverse-engineered nontrivial behaviors of transformer models. This paper systematizes the mechanistic interpretability process they followed. First, researchers choose a metric and dataset that elicit the desired model behavior.

In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts.

A highly performant object detector requires accurate ranking for the bounding box predictions.

Experiments show that the proposed ReBalanced Adversarial Training (ReBA T) can attain good robustness and does not suffer from robust overfitting even after very long training.

As mentioned in the discussion succeeding Corollary 3.6, we do conjecture the tight

Despite empirical advances, the theoretical study of frequentist LFI is still in its infancy.

Foundation models are trained on increasingly immense and opaque datasets.

In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems.