3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CL2023
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
Myles Foley, Ambrish Rawat, Taesung Lee +3
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption. While the pre-trained models can perform many tasks, such model…
cs.LG2020★ 3 cited
FAT: Federated Adversarial Training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn +1
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as…
cs.LG2019
Deep Latent Defence
Giulio Zizzo, Chris Hankin, Sergio Maffeis +1
Deep learning methods have shown state of the art performance in a range of tasks from computer vision to natural language processing. However, it is well known that such systems a…