13 citations · 27 across the 13 of their papers we have counts for
6 papers · 1 filter
SimpleFSDP: Simpler Fully Sharded Data Parallel with torch.compile
Ruisi Zhang, Tianyu Liu, Will Feng +4
Distributed training of large models consumes enormous computation resources and requires substantial engineering efforts to compose various training techniques. This paper present…
Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
Ruisi Zhang, Farinaz Koushanfar
The widely adopted and powerful generative large language models (LLMs) have raised concerns about intellectual property rights violations and the spread of machine-generated misin…
Automated Physical Design Watermarking Leveraging Graph Neural Networks
Ruisi Zhang, Rachel Selina Rajarathnam, David Z. Pan +1
This paper presents AutoMarks, an automated and transferable watermarking framework that leverages graph neural networks to reduce the watermark search overheads during the placeme…
ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection
Ruisi Zhang, Rachel Selina Rajarathnam, David Z. Pan +1
Physical design watermarking on contemporary integrated circuit (IC) layout encodes signatures without considering the dense connections and design constraints, which could lead to…
EmMark: Robust Watermarks for IP Protection of Embedded Quantized Large Language Models
Ruisi Zhang, Farinaz Koushanfar
This paper introduces EmMark,a novel watermarking framework for protecting the intellectual property (IP) of embedded large language models deployed on resource-constrained edge de…
Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models
Mingjia Huo, Sai Ashish Somayajula, Youwei Liang +3
Large language models generate high-quality responses with potential misinformation, underscoring the need for regulation by distinguishing AI-generated and human-written texts. Wa…