Search Results for author: Taolue Chen

Found 6 papers, 2 papers with code

QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks

1 code implementation10 Dec 2022 Yedi Zhang, Zhe Zhao, Fu Song, Min Zhang, Taolue Chen, Jun Sun

Experimental results on QNNs with different quantization bits confirm the effectiveness and efficiency of our approach, e. g., two orders of magnitude faster and able to solve more verification tasks in the same time limit than the state-of-the-art methods.


BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks

no code implementations12 Mar 2021 Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Taolue Chen

Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications.


Learning Safe Neural Network Controllers with Barrier Certificates

1 code implementation18 Sep 2020 Hengjun Zhao, Xia Zeng, Taolue Chen, Zhiming Liu, Jim Woodcock

We provide a novel approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties.

Finger Texture Biometric Characteristic: a Survey

no code implementations7 Jun 2020 Raid R. O. Al-Nima, Tingting Han, Taolue Chen, Satnam Dlay, Jonathon Chambers

\begin{abstract} In recent years, the Finger Texture (FT) has attracted considerable attention as a biometric characteristic.

Boosting API Recommendation with Implicit Feedback

no code implementations4 Feb 2020 Yu Zhou, Xinying Yang, Taolue Chen, Zhiqiu Huang, Xiaoxing Ma, Harald Gall

In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance.

Active Learning Learning-To-Rank

Making Agents' Abilities Explicit

no code implementations27 Nov 2018 Yedi Zhang, Fu Song, Taolue Chen

Alternating-time temporal logics (ATL/ATL*) represent a family of modal logics for reasoning about agents' strategic abilities in multiagent systems (MAS).

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