Search Results for author: Pei Huang

Found 7 papers, 1 papers with code

Towards Efficient Verification of Quantized Neural Networks

1 code implementation20 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.

Quantization

A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement

no code implementations21 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).

Adversarial Attack

A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking

no code implementations15 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.

Dialogue State Tracking Language Modelling

Quantifying Robustness to Adversarial Word Substitutions

no code implementations11 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.

Can Graph Neural Networks Learn to Solve MaxSAT Problem?

no code implementations15 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.

ε-weakened Robustness of Deep Neural Networks

no code implementations29 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).

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