1 code implementation • 14 Aug 2023 • Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation.
1 code implementation • 16 Aug 2021 • Fan Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Yonina C. Eldar, Stefano Buzzi
As the standardization of 5G is being solidified, researchers are speculating what 6G will be.
1 code implementation • 9 Jun 2021 • Yifei Li, Tao Du, Kui Wu, Jie Xu, Wojciech Matusik
This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
2 code implementations • ICML 2020 • Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik
Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal).
Multi-Objective Reinforcement Learning reinforcement-learning
1 code implementation • 26 Jun 2023 • Ankit Goyal, Jie Xu, Yijie Guo, Valts Blukis, Yu-Wei Chao, Dieter Fox
In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct).
Ranked #3 on Robot Manipulation on RLBench
1 code implementation • 15 Jul 2021 • Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal
Existing methods for co-optimization are limited and fail to explore a rich space of designs.
1 code implementation • CVPR 2022 • Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He
Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.
1 code implementation • 4 Nov 2021 • Tao Chen, Jie Xu, Pulkit Agrawal
The videos of the learned policies are available at: https://taochenshh. github. io/projects/in-hand-reorientation.
1 code implementation • 16 Jul 2022 • Jiazhen Liu, Xirong Li, Qijie Wei, Jie Xu, Dayong Ding
To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch.
Ranked #2 on Image Registration on FIRE
1 code implementation • 16 Jul 2019 • Mengwei Yang, Linqi Song, Jie Xu, Congduan Li, Guozhen Tan
Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.
1 code implementation • 26 Jul 2020 • Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu
Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.
1 code implementation • 28 Mar 2021 • Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu
To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.
1 code implementation • 16 May 2023 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes.
1 code implementation • 17 Mar 2022 • Liang Peng, Nan Wang, Jie Xu, Xiaofeng Zhu, Xiaoxiao Li
To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
1 code implementation • ICCV 2023 • Tao Xie, Kun Dai, Siyi Lu, Ke Wang, Zhiqiang Jiang, Jinghan Gao, Dedong Liu, Jie Xu, Lijun Zhao, Ruifeng Li
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task.
1 code implementation • IJCNLP 2019 • Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, Jie Xu
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts.
1 code implementation • 14 Jun 2022 • Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, Haiyong Xie
More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task.
1 code implementation • EMNLP 2020 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.
Dialogue State Tracking Multi-domain Dialogue State Tracking
1 code implementation • 8 Nov 2015 • Duorui Xie, Lingyu Liang, Lianwen Jin, Jie Xu, Mengru Li
In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception.
1 code implementation • 18 Jun 2021 • Ruiqing Ding, Yu Zhou, Jie Xu, Yan Xie, Qiqiang Liang, He Ren, YiXuan Wang, Yanlin Chen, Leye Wang, Man Huang
To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport theory and self-paced ensemble for Sepsis early detection, called SPSSOT, which can efficiently transfer knowledge from the source hospital (with rich labeled data) to the target hospital (with scarce labeled data).
1 code implementation • 30 Mar 2023 • Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, Xiaofeng Zhu
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners.
1 code implementation • 18 Oct 2023 • Hanbo Zhang, Jie Xu, Yuchen Mo, Tao Kong
Ambiguity is ubiquitous in human communication.
1 code implementation • 17 Apr 2024 • Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue.
no code implementations • 14 Mar 2018 • Zihao Liu, Tao Liu, Wujie Wen, Lei Jiang, Jie Xu, Yanzhi Wang, Gang Quan
To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification.
no code implementations • 17 Mar 2017 • Jie Xu, Lixing Chen, Shaolei Ren
Mobile edge computing (a. k. a.
no code implementations • 24 Jan 2017 • Linqi Song, Jie Xu
The key feature of our algorithm is that in addition to sending a query to an annotator for the ground truth, prior information about the ground truth learned by the learner is sent together, thereby reducing the query cost.
no code implementations • 5 Jul 2016 • Kalyani Nagaraj, Jie Xu, Raghu Pasupathy, Soumyadip Ghosh
The first of our proposed estimators $\estOpt$ is the "full-information" estimator that actively exploits such local structure to achieve bounded relative error in Gaussian settings.
no code implementations • 16 Aug 2015 • Yannick Meier, Jie Xu, Onur Atan, Mihaela van der Schaar
We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years.
no code implementations • 30 Dec 2015 • Jie Xu, Tianwei Xing, Mihaela van der Schaar
Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs.
no code implementations • 8 Nov 2015 • Jie Xu, Lianwen Jin, Lingyu Liang, Ziyong Feng, Duorui Xie
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem.
no code implementations • 22 Mar 2014 • Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li
This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media.
no code implementations • 7 Oct 2018 • Lixing Chen, Jie Xu, Shaolei Ren, Pan Zhou
To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm.
no code implementations • NeurIPS 2018 • Lixing Chen, Jie Xu, Zhuo Lu
In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions.
no code implementations • NeurIPS 2018 • Jie Xu, Lei Luo, Cheng Deng, Heng Huang
Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems.
no code implementations • 4 May 2019 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu
In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.
no code implementations • 13 Nov 2019 • Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.
no code implementations • 28 Jan 2020 • Xiaoran Cai, Xiaopeng Mo, Junyang Chen, Jie Xu
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources.
no code implementations • 9 Apr 2020 • Jie Xu, Heqiang Wang
This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data.
no code implementations • 25 Jun 2020 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu
In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.
no code implementations • 29 Feb 2020 • Xiaopeng Mo, Jie Xu
Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update.
Information Theory Signal Processing Information Theory
no code implementations • 21 Aug 2020 • Jie Xu, Wei zhang, Fei Wang
A popular distributed learning strategy is federated learning, where there is a central server storing the global model and a set of local computing nodes updating the model parameters with their corresponding data.
no code implementations • 19 Oct 2020 • Qingqing Wu, Jie Xu, Yong Zeng, Derrick Wing Kwan Ng, Naofal Al-Dhahir, Robert Schober, A. Lee Swindlehurst
On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
no code implementations • 2 Dec 2020 • Zhenglei He, Kim Phuc Tran, Sebastien Thomassey, Xianyi Zeng, Jie Xu, Changhai Yi
The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
no code implementations • 28 Feb 2020 • Hailiang Xie, Jie Xu, Ya-Feng Liu
This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with several multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference.
no code implementations • 29 Dec 2020 • Zhenglei He, Kim Phuc Tran, Sebastien Thomassey, Xianyi Zeng, Jie Xu, Chang Haiyi
Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies.
no code implementations • 4 Jan 2021 • Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, Jie Xu
Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.
no code implementations • 10 Jan 2021 • Jie Xu, Heqiang Wang, Lixing Chen
For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients.
no code implementations • 19 Jan 2021 • Setareh Maghsudi, Andrew Lan, Jie Xu, Mihaela van der Schaar
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal.
no code implementations • 2 Feb 2021 • Letian Zhang, Lixing Chen, Jie Xu
The basic idea of this system is to partition a deep neural network (DNN) into a front-end part running on the mobile device and a back-end part running on the edge server, with the key challenge being how to locate the optimal partition point to minimize the end-to-end inference delay.
no code implementations • 14 Apr 2021 • Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen
We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).
no code implementations • 8 Jun 2020 • Xiongfei Zhai, Xihan Chen, Jie Xu, Derrick Wing Kwan Ng
It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas.
no code implementations • 5 Jun 2020 • Ganggang Ma, Jie Xu, Ya-Feng Liu, Mohammad R. Vedady Moghadam
Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT).
no code implementations • 23 May 2021 • Jie Xu, Min Ding
Recent advancements in computational power and algorithms have enabled unabridged data (e. g., raw images or audio) to be used as input in some models (e. g., deep learning).
no code implementations • ICCV 2021 • Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He
The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.
no code implementations • 14 Jul 2021 • Jie Xu, Xingyu Chen, Xuguang Lan, Nanning Zheng
The experimental results show that our approach makes the interaction more efficient and safer.
no code implementations • 29 Sep 2021 • Michal Piovarci, Michael Foshey, Timothy Erps, Jie Xu, Vahid Babaei, Piotr Didyk, Wojciech Matusik, Szymon Rusinkiewicz, Bernd Bickel
We further show that in combination with reinforcement learning, our model can be used to discover control policies that outperform state-of-the-art controllers.
no code implementations • 15 Oct 2021 • Jieming Bian, Zhu Fu, Jie Xu
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years.
no code implementations • 25 Sep 2019 • Tao Du, Yunfei Li, Jie Xu, Andrew Spielberg, Kui Wu, Daniela Rus, Wojciech Matusik
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
no code implementations • 26 Nov 2021 • Xianxin Song, Ding Zhao, Haocheng Hua, Tony Xiao Han, Xun Yang, Jie Xu
This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked.
no code implementations • 2 Dec 2021 • Zhe Qu, Rui Duan, Lixing Chen, Jie Xu, Zhuo Lu, Yao Liu
In addition, client selection for HFL faces more challenges than conventional FL, e. g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO).
no code implementations • 11 Dec 2021 • Yang Bai, Lixing Chen, Shaolei Ren, Jie Xu
The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference with the learned knowledge.
no code implementations • 26 Dec 2021 • Shicheng Gao, Jie Xu, Xiaosen Li, Fangcheng Fu, Wentao Zhang, Wen Ouyang, Yangyu Tao, Bin Cui
For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.
no code implementations • 21 Jan 2022 • Peixi Liu, Guangxu Zhu, Wei Jiang, Wu Luo, Jie Xu, Shuguang Cui
This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC).
no code implementations • NeurIPS 2021 • Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik
In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots.
no code implementations • 12 Feb 2022 • Cong Shen, Jing Yang, Jie Xu
Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper.
no code implementations • ICLR 2022 • Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin
In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness.
no code implementations • 23 Apr 2022 • Xianxin Song, Jie Xu, Fan Liu, Tony Xiao Han, Yonina C. Eldar
This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region.
no code implementations • 26 May 2022 • Heqiang Wang, Jie Xu
Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency.
no code implementations • 9 Jun 2022 • Jieming Bian, Jie Xu
To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL.
no code implementations • 3 Jul 2022 • Dingzhu Wen, Peixi Liu, Guangxu Zhu, Yuanming Shi, Jie Xu, Yonina C. Eldar, Shuguang Cui
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
no code implementations • 12 Jul 2022 • Xianxin Song, Jie Xu, Fan Liu, Tony Xiao Han, Yonina C. Eldar
For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form.
no code implementations • 9 Oct 2022 • Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
no code implementations • 13 Oct 2022 • Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
no code implementations • 29 Oct 2022 • Xianxin Song, Tony Xiao Han, Jie Xu
This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communication (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one extended target at the non-line-of-sight (NLoS) region of the BS.
no code implementations • 7 Nov 2022 • Chengsheng Mao, Jie Xu, Luke Rasmussen, Yikuan Li, Prakash Adekkanattu, Jennifer Pacheco, Borna Bonakdarpour, Robert Vassar, Guoqian Jiang, Fei Wang, Jyotishman Pathak, Yuan Luo
Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020.
no code implementations • 4 Jan 2023 • Tianshu Chen, Hong Shen, Aiqun Hu, Weihang He, Jie Xu, Hongxing Hu
Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features.
no code implementations • 12 Jan 2023 • Ling Li, Lin Zhao, Linhao Xu, Jie Xu
Making top-down human pose estimation method present both good performance and high efficiency is appealing.
no code implementations • 21 Jan 2023 • Letian Zhang, Jie Xu
To achieve this goal, E$^3$Pose incorporates an attention-based LSTM to predict the occlusion information of each camera view and guide camera selection before cameras are selected to process the images of a scene, and runs a camera selection algorithm based on the Lyapunov optimization framework to make long-term adaptive selection decisions.
no code implementations • 14 Feb 2023 • Jieming Bian, Cong Shen, Jie Xu
In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e. g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients.
no code implementations • 23 Feb 2023 • Yingze Xie, Jie Xu, LiQiang Qiao, Yun Liu, Feiren Huang, Chaozhuo Li
Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content.
no code implementations • 28 Mar 2023 • Heqiang Wang, Jieming Bian, Jie Xu
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity.
no code implementations • 10 Apr 2023 • Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu
In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.
no code implementations • 21 Apr 2023 • Jieming Bian, Cong Shen, Jie Xu
The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection.
no code implementations • 26 Apr 2023 • Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu
In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
no code implementations • 12 May 2023 • Jie Xu, Lu Lu, Sen yang, Bilin Liang, Xinwei Peng, Jiali Pang, Jinru Ding, Xiaoming Shi, Lingrui Yang, Huan Song, Kang Li, Xin Sun, Shaoting Zhang
The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts.
no code implementations • 30 Jun 2023 • Xianxin Song, Xiaoqi Qin, Jie Xu, Rui Zhang
Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively.
no code implementations • 7 Jul 2023 • Yilong Chen, Huijun Xing, Jie Xu, Lexi Xu, Shuguang Cui
In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively.
no code implementations • 17 Jul 2023 • Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, Junbo Zhao
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate.
no code implementations • 10 Aug 2023 • Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu
This paper compares the signal-to-noise ratio (SNR) performance between the fully-passive intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing versus its semi-passive counterpart.
no code implementations • 15 Aug 2023 • Xiaoming Shi, Jie Xu, Jinru Ding, Jiali Pang, Sichen Liu, Shuqing Luo, Xingwei Peng, Lu Lu, Haihong Yang, Mingtao Hu, Tong Ruan, Shaoting Zhang
Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios.
no code implementations • 5 Sep 2023 • Shihang Lu, Fan Liu, Fuwang Dong, Yifeng Xiong, Jie Xu, Ya-Feng Liu
Radar systems typically employ well-designed deterministic signals for target sensing.
no code implementations • 24 Sep 2023 • Xinyue Chen, Jie Xu, Yazhou Ren, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
no code implementations • ICCV 2023 • Tao Xie, Ke Wang, Siyi Lu, Yukun Zhang, Kun Dai, Xiaoyu Li, Jie Xu, Li Wang, Lijun Zhao, Xinyu Zhang, Ruifeng Li
Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized.
no code implementations • ICCV 2023 • Guangnian Wan, Haitao Du, Xuejing Yuan, Jun Yang, Meiling Chen, Jie Xu
Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and independently distributed) data.
no code implementations • 20 Oct 2023 • Rui Du, Haocheng Hua, Hailiang Xie, Xianxin Song, Zhonghao Lyu, Mengshi Hu, Narengerile, Yan Xin, Stephen McCann, Michael Montemurro, Tony Xiao Han, Jie Xu
To resolve this issue, a new Task Group (TG), namely IEEE 802. 11bf, has been established by the IEEE 802. 11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications.
no code implementations • 2 Nov 2023 • Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, Hang Li, Tao Kong
We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.
no code implementations • 6 Nov 2023 • Jieming Bian, Lei Wang, Shaolei Ren, Jie Xu
Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repercussions.
no code implementations • 10 Nov 2023 • Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu, Tony Xiao Han, Derrick Wing Kwan Ng
Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized.
no code implementations • 21 Nov 2023 • Jinke Ren, Zezhong Zhang, Jie Xu, GuanYing Chen, Yaping Sun, Ping Zhang, Shuguang Cui
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks.
no code implementations • 10 Dec 2023 • Letian Zhang, Ming Li, Chen Chen, Jie Xu
This poses a paradox as the necessary camera pose must be estimated from the entire dataset, even though the data arrives sequentially and future chunks are inaccessible.
no code implementations • 9 Dec 2023 • Motoya Ohnishi, Iretiayo Akinola, Jie Xu, Ajay Mandlekar, Fabio Ramos
As a specific case of our framework, we devise a model predictive control method for path tracking.
no code implementations • 16 Dec 2023 • Lei Wang, Jieming Bian, Jie Xu
We introduce a novel algorithm called FedBeat (Federated Learning with Bayesian Ensemble-Assisted Transition Matrix Estimation).
no code implementations • 18 Dec 2023 • Heqiang Wang, Jie Xu
However, deep learning-based CSS methods often rely on centralized learning, posing challenges like communication overhead and data privacy risks.
no code implementations • 7 Jan 2024 • Yilong Chen, Zixiang Ren, Jie Xu, Yong Zeng, Derrick Wing Kwan Ng, Shuguang Cui
Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously.
no code implementations • 20 Jan 2024 • Xi Chen, MingKe You, Li Wang, Weizhi Liu, Yu Fu, Jie Xu, Shaoting Zhang, Gang Chen, Kang Li, Jian Li
This study focused on evaluating and enhancing the clinical capabilities of LLMs in specific domains, using osteoarthritis (OA) management as a case study.
no code implementations • 29 Jan 2024 • Ketul Shah, Robert Crandall, Jie Xu, Peng Zhou, Marian George, Mayank Bansal, Rama Chellappa
We report state-of-the-art results on the NTU-60, NTU-120 and ETRI datasets, as well as in the transfer learning setting on NUCLA, PKU-MMD-II and ROCOG-v2 datasets, demonstrating the robustness of our approach.
no code implementations • 24 Feb 2024 • Yuanzhe Peng, Jieming Bian, Jie Xu
The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics.
no code implementations • 14 Mar 2024 • Lei Wang, Jieming Bian, Letian Zhang, Chen Chen, Jie Xu
Federated learning (FL) allows collaborative machine learning training without sharing private data.
no code implementations • 1 Apr 2024 • Zhonghao Lyu, Yuchen Li, Guangxu Zhu, Jie Xu, H. Vincent Poor, Shuguang Cui
Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay.
no code implementations • 11 Apr 2024 • Tongzhou Mu, Yijie Guo, Jie Xu, Ankit Goyal, Hao Su, Dieter Fox, Animesh Garg
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning.