Search Results for author: Jie Xu

Found 110 papers, 23 papers with code

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

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

Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm

no code implementations1 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.


FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

no code implementations24 Feb 2024 Yuanzhe Peng, Jieming Bian, Jie Xu

The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics.

Federated Learning Privacy Preserving

MV2MAE: Multi-View Video Masked Autoencoders

no code implementations29 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.

Action Recognition Self-Supervised Learning +1

Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA

no code implementations20 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.

Management Retrieval

Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks

no code implementations7 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.


Online Vertical Federated Learning for Cooperative Spectrum Sensing

no code implementations18 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.

Vertical Federated Learning

Federated Learning with Instance-Dependent Noisy Label

no code implementations16 Dec 2023 Lei Wang, Jieming Bian, Jie Xu

We introduce a novel algorithm called FedBeat (Federated Learning with Bayesian Ensemble-Assisted Transition Matrix Estimation).

Federated Learning

IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment

no code implementations10 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.

Incremental Learning Knowledge Distillation

Knowledge Base Enabled Semantic Communication: A Generative Perspective

no code implementations21 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.

Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison

no code implementations10 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.

CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers

no code implementations6 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.

Federated Learning

Vision-Language Foundation Models as Effective Robot Imitators

no code implementations2 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.

Imitation Learning

An Overview on IEEE 802.11bf: WLAN Sensing

no code implementations20 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.


Federated Deep Multi-View Clustering with Global Self-Supervision

no code implementations24 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.


Sensing With Random Signals

no code implementations5 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.

OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes

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.

Multi-Task Learning Visual Localization

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

no code implementations15 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.

Language Modelling Large Language Model +1

Machine Unlearning: Solutions and Challenges

1 code implementation14 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.

Machine Unlearning

Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR Analysis

no code implementations10 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.

Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information

no code implementations7 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.

Cramér-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communications

no code implementations30 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.

RVT: Robotic View Transformer for 3D Object Manipulation

1 code implementation26 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).

Object Robot Manipulation

ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

1 code implementation16 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.

Contrastive Learning Few-Shot Text Classification +2

Towards clinical AI fairness: A translational perspective

no code implementations26 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.

Fairness Translation

Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning

no code implementations21 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.

Federated Learning

Accelerating Hybrid Federated Learning Convergence under Partial Participation

no code implementations10 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.

Federated Learning

On the Local Cache Update Rules in Streaming Federated Learning

no code implementations28 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.

Federated Learning Image Classification +2

Generative Sentiment Transfer via Adaptive Masking

no code implementations23 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.

Language Modelling

Federated Learning via Indirect Server-Client Communications

no code implementations14 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.

Federated Learning Privacy Preserving

E$^3$Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation

no code implementations21 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.

3D Pose Estimation

Towards High Performance One-Stage Human Pose Estimation

no code implementations12 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.

Human Detection Keypoint Detection +1

Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology

no code implementations4 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.


CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network

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.

Incremental Learning Multi-Task Learning

Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

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.

Federated Learning

AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease

no code implementations7 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.

Cramér-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communication with Extended Target

no code implementations29 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.

Variational Graph Generator for Multi-View Graph Clustering

no code implementations13 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.

Clustering Graph Clustering

Deep Clustering: A Comprehensive Survey

no code implementations9 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.

Clustering Deep Clustering

Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching

1 code implementation16 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.

Image Registration

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization

no code implementations12 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.

Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

no code implementations3 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.

Management Quantization

Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying

no code implementations9 Jun 2022 Jieming Bian, Jie Xu

To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL.

Attribute Federated Learning

Combating Client Dropout in Federated Learning via Friend Model Substitution

no code implementations26 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.

Federated Learning

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization

no code implementations23 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.

Accelerated Policy Learning with Parallel Differentiable Simulation

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.

GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis

1 code implementation17 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.

Classification Representation Learning +1

On Federated Learning with Energy Harvesting Clients

no code implementations12 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.

Federated Learning Scheduling

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

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.

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

no code implementations21 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).

K-Core Decomposition on Super Large Graphs with Limited Resources

no code implementations26 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.

Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement

no code implementations11 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.

Transfer Learning

Context-Aware Online Client Selection for Hierarchical Federated Learning

no code implementations2 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).

Federated Learning

Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication

no code implementations26 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.

A System for General In-Hand Object Re-Orientation

1 code implementation4 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.


FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

no code implementations15 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.

Ensemble Learning Federated Learning +1

Closed-Loop Control of Additive Manufacturing via Reinforcement Learning

no code implementations29 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.

reinforcement-learning Reinforcement Learning (RL)

An End-to-End Differentiable Framework for Contact-Aware Robot Design

1 code implementation15 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.

Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

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.


Multi-level Feature Learning for Contrastive Multi-view Clustering

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.

Clustering Contrastive Learning

Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble

1 code implementation18 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).

Domain Adaptation Semi-supervised Domain Adaptation +1

DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

1 code implementation9 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.

Transparent Model of Unabridged Data (TMUD)

no code implementations23 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).


Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

no code implementations14 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).

Federated Learning

Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering

1 code implementation28 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.


Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning

no code implementations2 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.

Decision Making object-detection +1

Personalized Education in the AI Era: What to Expect Next?

no code implementations19 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.

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

no code implementations10 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.

Fairness Federated Learning

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

no code implementations4 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.

Graph Attention Representation Learning +1

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

no code implementations29 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.

Decision Making

Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning

no code implementations2 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.

Multi-agent Reinforcement Learning

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

no code implementations19 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.

Neural Dialogue State Tracking with Temporally Expressive Networks

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.

Dialogue State Tracking

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

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

A(DP)$^2$SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy

no code implementations21 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.

Federated Learning

Deep Embedded Multi-view Clustering with Collaborative Training

1 code implementation26 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.


Adversarial Machine Learning based Partial-model Attack in IoT

no code implementations25 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.

BIG-bench Machine Learning Decision Making

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

no code implementations8 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.

Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

no code implementations5 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).

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

no code implementations9 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.

Federated Learning Stochastic Optimization

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

no code implementations29 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

Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming

no code implementations28 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.


D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

no code implementations28 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.

BIG-bench Machine Learning

Federated Learning for Healthcare Informatics

no code implementations13 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.

Federated Learning

D3PG: Deep Differentiable Deterministic Policy Gradients

no code implementations25 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.

Model Predictive Control

Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

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.

Denoising Relation +1

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

1 code implementation16 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.

Anomaly Detection Federated Learning +1

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

no code implementations4 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.

BIG-bench Machine Learning

Bilevel Distance Metric Learning for Robust Image Recognition

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.

Metric Learning

Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward

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.

Decision Making Multi-Armed Bandits +1

Spatio-temporal Edge Service Placement: A Bandit Learning Approach

no code implementations7 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.

Decision Making Edge-computing

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

no code implementations14 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.

Data Compression General Classification +2

A Contextual Bandit Approach for Stream-Based Active Learning

no code implementations24 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.

Active Learning Decision Making

Efficient Estimation in the Tails of Gaussian Copulas

no code implementations5 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.

Personalized Course Sequence Recommendations

no code implementations30 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.

Multi-Armed Bandits

A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model

no code implementations8 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.

Facial Beauty Prediction

SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

1 code implementation8 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.

Predicting Grades

no code implementations16 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.

Forecasting Popularity of Videos using Social Media

no code implementations22 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.

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