1 code implementation • 31 Mar 2024 • Shiwen Shan, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng
Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.
no code implementations • 9 Mar 2024 • Yichen Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Wenliang Zhong, Guannan Zhang
Then, the client trains the local model with both the cached samples and the samples from the new task.
no code implementations • 27 Feb 2024 • JunJie Huang, Jinyang Liu, Zhuangbin Chen, Zhihan Jiang, Yichen Li, Jiazhen Gu, Cong Feng, Zengyin Yang, Yongqiang Yang, Michael R. Lyu
To date, FaultProfIT has analyzed 10, 000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements.
no code implementations • 6 Feb 2024 • Yichen Li, Yun Peng, Yintong Huo, Michael R. Lyu
We conducted preliminary experiments to validate the performance of IDECoder and observed that this synergy represents a promising trend for future exploration.
no code implementations • 27 Jan 2024 • Zongjie Li, Wenying Qiu, Pingchuan Ma, Yichen Li, You Li, Sijia He, Baozheng Jiang, Shuai Wang, Weixi Gu
In this paper, we present a comprehensive empirical study on the accuracy and robustness of LLMs in the context of the Chinese industrial production area.
1 code implementation • 10 Jan 2024 • Jinyang Liu, Wenwei Gu, Zhuangbin Chen, Yichen Li, Yuxin Su, Michael R. Lyu
These methods are evaluated with five multivariate KPI datasets that are publicly available.
no code implementations • 28 Dec 2023 • Yichen Li, Chicheng Zhang
We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible.
no code implementations • 1 Nov 2023 • Cong Guan, Lichao Zhang, Chunpeng Fan, Yichen Li, Feng Chen, Lihe Li, Yunjia Tian, Lei Yuan, Yang Yu
Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence.
no code implementations • 27 Oct 2023 • Qiankun Liu, Yichen Li, Yuqi Jiang, Ying Fu
Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image.
no code implementations • 29 Sep 2023 • Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta, Wojciech Matusik
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
no code implementations • 27 Sep 2023 • Muyu Wang, Shiyu Fan, Yichen Li, Hui Chen
This study aimed to develop an efficient multi-modal fusion architecture for medical data that was robust to missing modalities and further improved the performance on disease diagnosis. X-ray chest radiographs for the image modality, radiology reports for the text modality, and structured value data for the tabular data modality were fused in this study.
1 code implementation • CVPR 2023 • Jianping Zhang, Jen-tse Huang, Wenxuan Wang, Yichen Li, Weibin Wu, Xiaosen Wang, Yuxin Su, Michael R. Lyu
However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples.
no code implementations • 10 Mar 2023 • Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao, Wojciech Matusik, Leonidas Guibas
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
no code implementations • CVPR 2023 • Haozhao Wang, Yichen Li, Wenchao Xu, Ruixuan Li, Yufeng Zhan, Zhigang Zeng
In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models.
no code implementations • 26 Sep 2022 • Yichen Li, Chicheng Zhang
We make the following contributions: (1) we show that in the $\textbf{COIL}$ problem, any proper online learning algorithm cannot guarantee a sublinear regret in general; (2) we propose $\textbf{Logger}$, an improper online learning algorithmic framework, that reduces $\textbf{COIL}$ to online linear optimization, by utilizing a new definition of mixed policy class; (3) we design two oracle-efficient algorithms within the $\textbf{Logger}$ framework that enjoy different sample and interaction round complexity tradeoffs, and conduct finite-sample analyses to show their improvements over naive behavior cloning; (4) we show that under the standard complexity-theoretic assumptions, efficient dynamic regret minimization is infeasible in the $\textbf{Logger}$ framework.
1 code implementation • 2 Aug 2021 • Zhen Li, Jing Tang, Deqing Zou, Qian Chen, Shouhuai Xu, Chao Zhang, Yichen Li, Hai Jin
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
no code implementations • 13 Aug 2020 • Yichen Li, Xingchao Peng
Deep networks have been used to learn transferable representations for domain adaptation.
1 code implementation • ECCV 2020 • Xingchao Peng, Yichen Li, Kate Saenko
Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model.
1 code implementation • ECCV 2020 • Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung, Leonidas Guibas
Autonomous assembly is a crucial capability for robots in many applications.
no code implementations • 10 Dec 2019 • Yichen Li, Xingchao Peng
Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy.
no code implementations • ICLR 2019 • Hongyin Luo, Yichen Li, Jie Fu, James Glass
Recently, there have been some attempts to use non-recurrent neural models for language modeling.
3 code implementations • 17 Nov 2018 • Bryan A. Plummer, Kevin J. Shih, Yichen Li, Ke Xu, Svetlana Lazebnik, Stan Sclaroff, Kate Saenko
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image.
1 code implementation • 4 Feb 2016 • Yichen Li, Craig Thorn, Wei Tang, Jyoti Joshi, Xin Qian, Milind Diwan, Steve Kettell, William Morse, Triveni Rao, James Stewart, Thomas Tsang, Lige Zhang
We describe the design of a 20-liter test stand constructed to study fundamental properties of liquid argon (LAr).
Instrumentation and Detectors High Energy Physics - Experiment