no code implementations • 26 Apr 2024 • Quan Zhang, Binqi Zeng, Chijin Zhou, Gwihwan Go, Heyuan Shi, Yu Jiang
Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique.
no code implementations • 25 Apr 2024 • Yu Jiang, Jie Liang, Fuchen Ma, Yuanliang Chen, Chijin Zhou, Yuheng Shen, Zhiyong Wu, Jingzhou Fu, Mingzhe Wang, Shanshan Li, Quan Zhang
Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs).
1 code implementation • 20 Feb 2024 • Jinjing Shi, Zimeng Xiao, Heyuan Shi, Yu Jiang, Xuelong Li
Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement sufficiency and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples.
no code implementations • 16 Jan 2024 • Yu Jiang, Jiyuan Shen, Ziyao Liu, Chee Wei Tan, Kwok-Yan Lam
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model.
no code implementations • 16 Oct 2023 • Yingwei Ma, Yue Yu, Shanshan Li, Yu Jiang, Yong Guo, Yuanliang Zhang, Yutao Xie, Xiangke Liao
Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i. e., in-context learning), while being generalizable and applicable to challenging domains.
1 code implementation • 28 Sep 2023 • Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, Shanshan Li
Inspired by the great success of code data in training LLMs, we naturally wonder at which training stage introducing code data can really help LLMs reasoning.
no code implementations • 19 Jul 2023 • Qifang Zhao, Tianyu Li, Meng Du, Yu Jiang, Qinghui Sun, Zhongyao Wang, Hong Liu, Huan Xu
When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost.
no code implementations • 25 Mar 2023 • Ertai Liu, Josephine Monica, Kaitlin Gold, Lance Cadle-Davidson, David Combs, Yu Jiang
Autonomous navigation is the key to achieving the full automation of agricultural research and production management (e. g., disease management and yield prediction) using agricultural robots.
no code implementations • 17 Feb 2023 • Shengqin Wang, Yongji Zhang, Hong Qi, Minghao Zhao, Yu Jiang
With multiple spatial static hypergraphs and dynamic TPH, our network can learn more complete spatial-temporal features.
no code implementations • 18 Nov 2022 • Huigen Ye, Hongyan Wang, Hua Xu, Chengming Wang, Yu Jiang
Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs.
1 code implementation • 19 Oct 2022 • Xueru Wen, Changjiang Zhou, Haotian Tang, Luguang Liang, Yu Jiang, Hong Qi
Named entity recognition is a traditional task in natural language processing.
1 code implementation • 19 Oct 2022 • Xueru Wen, Changjiang Zhou, Haotian Tang, Luguang Liang, Yu Jiang, Hong Qi
Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts.
no code implementations • 13 Oct 2022 • Yusen Wang, Zongcheng Li, Yu Jiang, Kaixuan Zhou, Tuo Cao, Yanping Fu, Chunxia Xiao
Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity.
no code implementations • 22 Jul 2022 • Yu Jiang, John Eisenmann, William Graves, Vijayaraghavan Sridhar, Zackary Anderson
Second, an algorithm is developed to extract a pitch profile from the road height profile data.
1 code implementation • 31 May 2022 • Shengqin Wang, Yongji Zhang, Minghao Zhao, Hong Qi, Kai Wang, Fenglin Wei, Yu Jiang
Skeleton-based action recognition methods are limited by the semantic extraction of spatio-temporal skeletal maps.
Ranked #7 on Skeleton Based Action Recognition on N-UCLA
no code implementations • 23 May 2022 • Yasushi Ota, Yu Jiang, Daiki Maki
We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in different financial markets using a Bayesian inference approach, which is presented as an IOP solution.
1 code implementation • 28 Apr 2022 • Nachuan Ma, Jiahe Fan, Wenshuo Wang, Jin Wu, Yu Jiang, Lihua Xie, Rui Fan
Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades.
no code implementations • 17 Mar 2022 • Mingzhuang Hua, Francisco Camara Pereira, Yu Jiang, Xuewu Chen
The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist.
no code implementations • 9 Jan 2022 • Yu Jiang, Christian Poellabauer
Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes.
1 code implementation • 6 Dec 2021 • Yongqiang Tian, Wuqi Zhang, Ming Wen, Shing-Chi Cheung, Chengnian Sun, Shiqing Ma, Yu Jiang
To this end, we propose DFLARE, a novel, search-based, black-box testing technique to automatically find triggering inputs that result in deviated behaviors in image classification tasks.
no code implementations • 29 Sep 2021 • Qifang Zhao, Yu Jiang, Yuqing Liu, Meng Du, Qinghui Sun, Chao Xu, Huan Xu, Zhongyao Wang
Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.
no code implementations • 18 Aug 2021 • Yu Jiang, Lei Hu, Yongmei Zhang, Xin Yang
With the purpose of improving change detection effectiveness of the model in the multi-resolution data set, a weighted rich-scale inception coder network (WRICNet) is proposed in this article, which can make a great fusion of shallow multi-scale features, and deep multi-scale features.
no code implementations • 29 Apr 2021 • Zhiyuan Wu, Yu Jiang, Minghao Zhao, Chupeng Cui, Zongmin Yang, Xinhui Xue, Hong Qi
To further improve the robustness of the student, we extend SD to Enhanced Spirit Distillation (ESD) in exploiting a more comprehensive knowledge by introducing the proximity domain which is similar to the target domain for feature extraction.
no code implementations • 25 Mar 2021 • Zhiyuan Wu, Yu Jiang, Chupeng Cui, Zongmin Yang, Xinhui Xue, Hong Qi
Inspired by the ideas of Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge distillation, we propose a new knowledge distillation method for cross-domain knowledge transference and efficient data-insufficient network training, named Spirit Distillation(SD), which allow the student network to mimic the teacher network to extract general features, so that a compact and accurate student network can be trained for real-time semantic segmentation of road scenes.
no code implementations • 15 Mar 2021 • Dongning Ma, Jianmin Guo, Yu Jiang, Xun Jiao
Using handwritten digit classification as an example, we show that HDTest can generate thousands of adversarial inputs with negligible perturbations that can successfully fool HDC models.
no code implementations • 3 Jan 2021 • Siyu Chen, Ravi Seshadri, Carlos Lima Azevedo, Arun P. Akkinepally, Renming Liu, Andrea Araldo, Yu Jiang, Moshe E. Ben-Akiva
Further, it is more robust in the presence of forecasting errors and non-recurrent events due to the adaptiveness of the market.
no code implementations • ICCV 2021 • Siqi Li, Yutong Feng, Yipeng Li, Yu Jiang, Changqing Zou, Yue Gao
Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera.
no code implementations • 26 Oct 2020 • Zhiyuan Wu, Hong Qi, Yu Jiang, Minghao Zhao, Chupeng Cui, Zongmin Yang, Xinhui Xue
Model compression becomes a recent trend due to the requirement of deploying neural networks on embedded and mobile devices.
no code implementations • 31 Aug 2020 • Inon Peled, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco C. Pereira
Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.
no code implementations • 11 Mar 2020 • Lin Jia, Kewen Li, Yu Jiang, Xin Guo, Ting zhao
According to the current trend, based on the three models, the total number of people expected to be infected is 49852-57447 in Wuhan, 12972-13405 in non-Hubei areas and 80261-85140 in China respectively.
Populations and Evolution
no code implementations • 24 Jan 2020 • Zhensong Wei, Yu Jiang, Xishun Liao, Xuewei Qi, Ziran Wang, Guoyuan Wu, Peng Hao, Matthew Barth
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.
no code implementations • 11 Nov 2019 • Jianmin Guo, Yue Zhao, Quan Zhang, Yu Jiang
Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4. 17% to 97. 22% higher success (or generation) rate.
no code implementations • 25 Sep 2019 • Qucheng Gong, Yu Jiang, Yuandong Tian
While playing is relatively easy for modern software, bidding is challenging and requires agents to learn a communication protocol to reach the optimal contract jointly, with their own private information.
3 code implementations • 3 Jun 2019 • Jie Ren, Fei Zhou, Xiaoxi Li, Qi Chen, Hongmei Zhang, Shuangge Ma, Yu Jiang, Cen Wu
Existing Bayesian methods for G$\times$E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences.
Methodology
7 code implementations • CVPR 2019 • Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, Marcus Rohrbach
We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset.
Ranked #3 on Visual Question Answering (VQA) on VizWiz 2018
no code implementations • 26 Feb 2019 • Inon Peled, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco C. Pereira
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements.
no code implementations • 31 Jan 2019 • Xiaoning Du, Bihuan Chen, Yuekang Li, Jianmin Guo, Yaqin Zhou, Yang Liu, Yu Jiang
The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.
Software Engineering
1 code implementation • 28 Aug 2018 • Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, Jiaguang Sun
Deep learning (DL) systems are increasingly applied to safety-critical domains such as autonomous driving cars.
Software Engineering
9 code implementations • 26 Jul 2018 • Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, Devi Parikh
We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2. 0 dataset -- from 65. 67% to 70. 22%.
Ranked #10 on Visual Question Answering (VQA) on A-OKVQA
no code implementations • 30 Jun 2018 • Yuanliang Chen, Yu Jiang, Jie Liang, Mingzhe Wang, Xun Jiao
For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.
Software Engineering