no code implementations • 23 Feb 2024 • Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
no code implementations • 29 Jun 2023 • Bowen Yu, Cheng Fu, Haiyang Yu, Fei Huang, Yongbin Li
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data.
no code implementations • 23 Feb 2023 • Yeqin Zhang, Haomin Fu, Cheng Fu, Haiyang Yu, Yongbin Li, Cam-Tu Nguyen
Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5).
1 code implementation • 17 Feb 2023 • Moritz Neun, Christian Eichenberger, Yanan Xin, Cheng Fu, Nina Wiedemann, Henry Martin, Martin Tomko, Lukas Ambühl, Luca Hermes, Michael Kopp
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce.
no code implementations • ICCV 2023 • Cheng Fu, Hanxian Huang, Zixuan Jiang, Yun Ni, Lifeng Nai, Gang Wu, Liqun Cheng, Yanqi Zhou, Sheng Li, Andrew Li, Jishen Zhao
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
no code implementations • 14 Jul 2022 • Zhenyu Zhang, Bowen Yu, Haiyang Yu, Tingwen Liu, Cheng Fu, Jingyang Li, Chengguang Tang, Jian Sun, Yongbin Li
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
no code implementations • 12 May 2022 • Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
1 code implementation • 1 Jan 2021 • Cheng Fu, Kunlin Yang, Xinyun Chen, Yuandong Tian, Jishen Zhao
In software development, decompilation aims to reverse engineer binary executables.
no code implementations • ICCV 2021 • Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar
In this work, we present ProFlip, the first targeted Trojan attack framework that can divert the prediction of the DNN to the target class by progressively identifying and flipping a small set of bits in model parameters.
no code implementations • 31 Oct 2020 • Cheng Fu, Robert Weibel
The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions.
no code implementations • NeurIPS 2019 • Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.
no code implementations • 28 Jun 2019 • Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
Reverse engineering of binary executables is a critical problem in the computer security domain.
no code implementations • 4 Oct 2018 • Cheng Fu, Shilin Zhu, Hao Su, Ching-En Lee, Jishen Zhao
Thus there does exist redundancy that can be exploited to further reduce the amount of on-chip computations.