CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X

1 code implementation • 30 Mar 2023 • Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.

Code Generation

GIPA: A General Information Propagation Algorithm for Graph Learning

1 code implementation • 19 Jan 2023 • Houyi Li, Zhihong Chen, Zhao Li, Qinkai Zheng, Peng Zhang, Shuigeng Zhou

Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.

Graph Learning Link Prediction +1

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

1 code implementation • 8 Nov 2021 • Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang

To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.

Adversarial Robustness Benchmarking +1

TDGIA:Effective Injection Attacks on Graph Neural Networks

1 code implementation • 12 Jun 2021 • Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang

In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.

Adversarial Attack

GIPA: General Information Propagation Algorithm for Graph Learning

2 code implementations • 13 May 2021 • Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu

Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.

Graph Attention Graph Learning +2

Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques

1 code implementation • 27 May 2020 • Han Qiu, Yi Zeng, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, Gerard Memmi

Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

no code implementations • 20 Mar 2020 • Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch

This report is about applications based on spatial-frequency transform and deep learning techniques.

Denoising