no code implementations • 9 May 2024 • Xinwei Zhang, Aishan Liu, Tianyuan Zhang, Siyuan Liang, Xianglong Liu
Existing backdoor attack methods on LD exhibit limited effectiveness in dynamic real-world scenarios, primarily because they fail to consider dynamic scene factors, including changes in driving perspectives (e. g., viewpoint transformations) and environmental conditions (e. g., weather or lighting changes).
no code implementations • 28 Feb 2024 • Zhiqi Bu, Xinwei Zhang, Mingyi Hong, Sheng Zha, George Karypis
The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that requires formal protection.
no code implementations • 12 Feb 2024 • Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konaković Luković
In this paper, we observe that in such scenarios optimal solution typically lies on the boundary between feasible and infeasible regions of the design space, making it considerably more difficult than that with interior optima.
no code implementations • 23 Dec 2023 • Aishan Liu, Xinwei Zhang, Yisong Xiao, Yuguang Zhou, Siyuan Liang, Jiakai Wang, Xianglong Liu, Xiaochun Cao, DaCheng Tao
This paper aims to raise awareness of the potential threats associated with applying PVMs in practical scenarios.
no code implementations • 24 Nov 2023 • Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong
In our work, we propose a new error-feedback (EF) DP algorithm as an alternative to DPSGD-GC, which not only offers a diminishing utility bound without inducing a constant clipping bias, but more importantly, it allows for an arbitrary choice of clipping threshold that is independent of the problem.
no code implementations • 14 Nov 2023 • Ye Tian, Xinwei Zhang, Zhiqiang Tan
Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors.
1 code implementation • 15 Sep 2023 • Kristoffer Larsen, Chen Zhao, Joyce Keyak, Qiuying Sha, Diana Paez, Xinwei Zhang, Guang-Uei Hung, Jiangang Zou, Amalia Peix, Weihua Zhou
Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary.
no code implementations • 2 Jun 2023 • Zhuo He, Hongjin Si, Xinwei Zhang, Qing-Hui Chen, Jiangang Zou, Weihua Zhou
The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response.
no code implementations • 4 May 2023 • Kristoffer Larsena, Zhuo He, Chen Zhao, Xinwei Zhang, Quiying Sha, Claudio T Mesquitad, Diana Paeze, Ernest V. Garciaf, Jiangang Zou, Amalia Peix, Weihua Zhou
The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps.
1 code implementation • 21 Apr 2023 • Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo. Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation.
no code implementations • 16 Mar 2023 • Xinwei Zhang, Mingyi Hong, Jie Chen
In this paper, we propose a model splitting method that splits a backbone GNN across the clients and the server and a communication-efficient algorithm, GLASU, to train such a model.
no code implementations • 6 Nov 2022 • Xinwei Zhang, Guyue Li, Junqing Zhang, Linning Peng, Aiqun Hu, Xianbin Wang
Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems.
no code implementations • 9 Oct 2022 • Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.
1 code implementation • 17 Jul 2022 • Yinghui Li, Shulin Huang, Xinwei Zhang, Qingyu Zhou, Yangning Li, Ruiyang Liu, Yunbo Cao, Hai-Tao Zheng, Ying Shen
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
no code implementations • 27 Apr 2022 • Xinwei Zhang, Mingyi Hong, Nicola Elia
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control.
no code implementations • 25 Jun 2021 • Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, JinFeng Yi
Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL.
no code implementations • 1 Jun 2021 • Zhuo He, Xinwei Zhang, Chen Zhao, Zhiyong Qian, Yao Wang, Xiaofeng Hou, Jiangang Zou, Weihua Zhou
Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters.
no code implementations • 22 Dec 2020 • Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen
To the best of our knowledge, this is the first formulation and algorithm developed for the hybrid FL.
no code implementations • 15 Jun 2020 • Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
1 code implementation • 22 May 2020 • Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, Yang Liu
Aiming at designing FL algorithms that are provably fast and require as few assumptions as possible, we propose a new algorithm design strategy from the primal-dual optimization perspective.
no code implementations • 14 Jan 2020 • Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, Songtao Lu
In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i. e., problem classes), processing batch and streaming data (i. e., data types), over the networks in a distributed manner (i. e., communication and computation paradigm).
no code implementations • 24 Dec 2019 • Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, Qiang Yang
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.
no code implementations • 19 Jun 2019 • Xinwei Zhang, Zhiqiang Tan
Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training.