Qwen2 Technical Report

3 code implementations • 15 Jul 2024 • An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng, Rui Men, Ruize Gao, Runji Lin, Shijie Wang, Shuai Bai, Sinan Tan, Tianhang Zhu, TianHao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xuancheng Ren, Xuejing Liu, Yang Fan, Yang Yao, Yichang Zhang, Yu Wan, Yunfei Chu, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zhifang Guo, Zhihao Fan

This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.

GSM8K Language Modelling +2

Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation

1 code implementation • 5 Mar 2024 • Keke Huang, Ruize Gao, Bogdan Cautis, Xiaokui Xiao

Furthermore, we undertake an analysis of the approximation error of FIM for network inference.

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems

1 code implementation • 17 Oct 2023 • Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Gouping Huang, Shuming Shi, Jiajun Chen, ShuJian Huang

We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.

Machine Translation Translation

Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

1 code implementation • 22 May 2023 • Ruize Gao, Zhirui Zhang, Yichao Du, Lemao Liu, Rui Wang

Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval.

Domain Adaptation Machine Translation +3

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

1 code implementation • 15 Jun 2022 • Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available.

Adversarial Robustness Computational Efficiency

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

1 code implementation • 6 Oct 2021 • Jingwen Li, Yining Ma, Ruize Gao, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.

Decoder reinforcement-learning +1

Local Reweighting for Adversarial Training

no code implementations • 30 Jun 2021 • Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng

However, when tested on attacks different from the given attack simulated in training, the robustness may drop significantly (e. g., even worse than no reweighting).

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

2 code implementations • 22 Oct 2020 • Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data.

Adversarial Attack Detection