Search Results for author:
Found 3 papers, 1 papers with code
Exploring prompts to elicit memorization in masked language model-based named entity recognition
Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set.
Specification Overfitting in Artificial Intelligence
Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency.
WAFFLE: Watermarking in Federated Learning
In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
