no code implementations • 25 Jan 2024 • Haoze Wu, Omri Isac, Aleksandar Zeljić, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, Clark Barrett
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
1 code implementation • 20 Dec 2023 • Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark Barrett
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory.
no code implementations • 21 Mar 2022 • Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin, Jin Song Dong, Feifei Ma, Jian Zhang
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
no code implementations • 15 Jan 2022 • Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li, Tat-Seng Chua
In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.
no code implementations • 11 Jan 2022 • Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Meishan Zhang, Jian Zhang, Jintao Li
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
no code implementations • 15 Nov 2021 • Minghao Liu, Fuqi Jia, Pei Huang, Fan Zhang, Yuchen Sun, Shaowei Cai, Feifei Ma, Jian Zhang
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
no code implementations • 29 Oct 2021 • Pei Huang, Yuting Yang, Minghao Liu, Fuqi Jia, Feifei Ma, Jian Zhang
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).