no code implementations • 4 Jan 2024 • Yuxuan Liu, Haozhao Wang, Shuang Wang, Zhiming He, Wenchao Xu, Jialiang Zhu, Fan Yang
Estimating causal effects among different events is of great importance to critical fields such as drug development.
no code implementations • 31 Dec 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Song Guo
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.
no code implementations • 31 Dec 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Penghui Ruan, Song Guo
Selecting proper clients to participate in the iterative federated learning (FL) rounds is critical to effectively harness a broad range of distributed datasets.
no code implementations • 27 Dec 2023 • Xin Yuan, Ning li, Kang Wei, Wenchao Xu, Quan Chen, Hao Chen, Song Guo
The model segmentation without user mobility has been investigated deeply by previous works.
no code implementations • 2 May 2023 • Jingcai Guo, Yuanyuan Xu, Wenchao Xu, Yufeng Zhan, Yuxia Sun, Song Guo
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively.
no code implementations • 1 May 2023 • Jie Zhang, Xiaosong Ma, Song Guo, Wenchao Xu
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data.
no code implementations • 20 Mar 2023 • Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang, Yunfeng Fan, Song Guo
In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.
no code implementations • 14 Mar 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Junxiao Wang, Song Guo
Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.
1 code implementation • 9 Mar 2023 • Xiuyu Yang, Zhuangyan Zhang, Haikuo Du, Sui Yang, Fengping Sun, Yanbo Liu, Ling Pei, Wenchao Xu, Weiqi Sun, Zhengyu Li
Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion.
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 • 30 Nov 2022 • Xin Xie, Cunqing Hua, Pengwenlong Gu, Wenchao Xu
Blockchain has been deemed as a promising solution for providing security and privacy protection in the next-generation wireless networks.
no code implementations • 19 Nov 2022 • Fushuo Huo, Wenchao Xu, Song Guo, Jingcai Guo, Haozhao Wang, Ziming Liu, Xiaocheng Lu
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions.
no code implementations • 15 Nov 2022 • Jinyu Chen, Wenchao Xu, Song Guo, Junxiao Wang, Jie Zhang, Haozhao Wang
Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data.
no code implementations • CVPR 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, Song Guo
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.
no code implementations • 13 Nov 2022 • Leijie Wu, Song Guo, Yaohong Ding, Junxiao Wang, Wenchao Xu, Richard Yida Xu, Jie Zhang
In contrast, visual data exhibits a fundamentally different structure: Its basic unit (pixel) is a natural low-level representation with significant redundancies in the neighbourhood, which poses obvious challenges to the interpretability of MSA mechanism in ViT.
1 code implementation • 2 Nov 2022 • Ziyou Ren, Nan Cheng, Ruijin Sun, Xiucheng Wang, Ning Lu, Wenchao Xu
Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems.
no code implementations • 24 Aug 2022 • Tao Guo, Song Guo, Junxiao Wang, Wenchao Xu
Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training.
no code implementations • 27 Feb 2022 • Tao Guo, Song Guo, Jiewei Zhang, Wenchao Xu, Junxiao Wang
Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a learnt model will not expose user's privacy at the sample level.