no code implementations • 28 May 2024 • Xiumei Deng, Jun Li, Long Shi, Kang Wei, Ming Ding, Yumeng Shao, Wen Chen, Shi Jin
To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices.
no code implementations • 28 May 2024 • Xiumei Deng, Jun Li, Kang Wei, Long Shi, Zeihui Xiong, Ming Ding, Wen Chen, Shi Jin, H. Vincent Poor
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
1 code implementation • 21 May 2024 • Yuwen Qian, Shuchi Wu, Kang Wei, Ming Ding, Di Xiao, Tao Xiang, Chuan Ma, Song Guo
To tackle this issue, we dive into the fundamental mechanism of backdoor attacks on FSSL, proposing the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models.
no code implementations • 11 May 2024 • Yumeng Shao, Jun Li, Long Shi, Kang Wei, Ming Ding, Qianmu Li, Zengxiang Li, Wen Chen, Shi Jin
To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function.
1 code implementation • 7 May 2024 • Zhuoyi Yang, Heyang Jiang, Wenyi Hong, Jiayan Teng, Wendi Zheng, Yuxiao Dong, Ming Ding, Jie Tang
However, due to a quadratic increase in memory during generating ultra-high-resolution images (e. g. 4096*4096), the resolution of generated images is often limited to 1024*1024.
no code implementations • 15 Apr 2024 • Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
no code implementations • 23 Mar 2024 • Youyang Qu, Ming Ding, Nan Sun, Kanchana Thilakarathna, Tianqing Zhu, Dusit Niyato
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
no code implementations • 8 Mar 2024 • Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang, Jidong Chen, Xiaotao Gu, Yuxiao Dong, Ming Ding, Jie Tang
Recent advancements in text-to-image generative systems have been largely driven by diffusion models.
no code implementations • 27 Feb 2024 • Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song, Bin Xu, Yuxiao Dong, Jie Tang
Is there a general framework that can incorporate all existing negative sampling methods?
no code implementations • 16 Feb 2024 • Songjie Xie, Youlong Wu, Jiaxuan Li, Ming Ding, Khaled B. Letaief
Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP.
no code implementations • 8 Feb 2024 • Yasas Supeksala, Dinh C. Nguyen, Ming Ding, Thilina Ranbaduge, Calson Chua, Jun Zhang, Jun Li, H. Vincent Poor
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
1 code implementation • 6 Feb 2024 • Ji Qi, Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong, Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, Jie Tang
Drawing inspiration from human cognition in solving visual problems (e. g., marking, zoom in), this paper introduces Chain of Manipulations, a mechanism that enables VLMs to solve problems step-by-step with evidence.
1 code implementation • 14 Dec 2023 • Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxuan Zhang, Juanzi Li, Bin Xu, Yuxiao Dong, Ming Ding, Jie Tang
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #15 on Visual Question Answering on MM-Vet
1 code implementation • 13 Dec 2023 • Xin You, Ming Ding, Minghui Zhang, Hanxiao Zhang, Yi Yu, Jie Yang, Yun Gu
Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice.
no code implementations • 1 Dec 2023 • Shuchi Wu, Chuan Ma, Kang Wei, Xiaogang Xu, Ming Ding, Yuwen Qian, Tao Xiang
This paper introduces RDA, a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders: (1) suboptimal performances attributed to biased optimization objectives, and (2) elevated query costs stemming from the end-to-end paradigm that necessitates querying the target encoder every epoch.
no code implementations • 11 Nov 2023 • Yingjie Niu, Ming Ding, Keisuke Fujii, Kento Ohtani, Alexander Carballo, Kazuya Takeda
The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.
no code implementations • 7 Nov 2023 • Huan Tian, Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou
It leverages the difference in the predictions from both the original and fairness-enhanced models and exploits the observed prediction gaps as attack clues.
1 code implementation • 6 Nov 2023 • Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, Jie Tang
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on Visual Question Answering (VQA) on InfiMM-Eval
no code implementations • 13 Oct 2023 • Jixuan Cui, Jun Li, Zhen Mei, Kang Wei, Sha Wei, Ming Ding, Wen Chen, Song Guo
However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model.
1 code implementation • MICCAI 2023 • Xin You, Ming Ding, Minghui Zhang, Yangqian Wu, Yi Yu, Yun Gu, Jie Yang
In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset.
no code implementations • 20 Sep 2023 • Shiying Zhang, Jun Li, Long Shi, Ming Ding, Dinh C. Nguyen, Wuzheng Tan, Jian Weng, Zhu Han
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
no code implementations • 7 Sep 2023 • Luping Rao, Chuan Ma, Ming Ding, Yuwen Qian, Lu Zhou, Zhe Liu
However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns.
1 code implementation • 6 Sep 2023 • Zhen Yang, Ming Ding, Qingsong Lv, Zhihuan Jiang, Zehai He, Yuyi Guo, Jinfeng Bai, Jie Tang
Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools.
no code implementations • 5 Sep 2023 • Zhengrong Song, Chuan Ma, Ming Ding, Howard H. Yang, Yuwen Qian, Xiangwei Zhou
This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.
1 code implementation • 4 Sep 2023 • Jiayan Teng, Wendi Zheng, Ming Ding, Wenyi Hong, Jianqiao Wangni, Zhuoyi Yang, Jie Tang
Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation.
Ranked #1 on Image Generation on CelebA-HQ 256x256
no code implementations • 4 Aug 2023 • Xuefeng Han, Jun Li, Wen Chen, Zhen Mei, Kang Wei, Ming Ding, H. Vincent Poor
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.
no code implementations • 18 Jul 2023 • Kecheng Fan, Wen Chen, Jun Li, Xiumei Deng, Xuefeng Han, Ming Ding
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users.
no code implementations • 18 Jul 2023 • Yingjie Niu, Ming Ding, Maoning Ge, Robin Karlsson, Yuxiao Zhang, Kazuya Takeda
Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps.
no code implementations • 8 Jun 2023 • Hao Yu, Chuan Ma, Meng Liu, Tianyu Du, Ming Ding, Tao Xiang, Shouling Ji, Xinwang Liu
Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).
1 code implementation • 6 Jun 2023 • Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang
To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances.
no code implementations • 12 May 2023 • Youyang Qu, Xin Yuan, Ming Ding, Wei Ni, Thierry Rakotoarivelo, David Smith
This inspired recent research on removing the influence of specific data samples from a trained ML model.
1 code implementation • NeurIPS 2023 • Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong
We present a comprehensive solution to learn and improve text-to-image models from human preference feedback.
no code implementations • 9 Apr 2023 • Kang Wei, Jun Li, Chuan Ma, Ming Ding, Feng Shu, Haitao Zhao, Wen Chen, Hongbo Zhu
Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.
no code implementations • 7 Mar 2023 • Xin Yuan, Wei Ni, Ming Ding, Kang Wei, Jun Li, H. Vincent Poor
The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
no code implementations • 27 Feb 2023 • Guodong Huang, Chuan Ma, Ming Ding, Yuwen Qian, Chunpeng Ge, Liming Fang, Zhe Liu
To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead.
no code implementations • 13 Nov 2022 • Thilina Ranbaduge, Ming Ding
Thus, in this paper, we aim to explore how to protect the privacy of individual organisation data in a differential privacy (DP) setting.
no code implementations • 4 Nov 2022 • Ziyan Yin, Zhe Wang, Jun Li, Ming Ding, Wen Chen, Shi Jin
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks.
no code implementations • 3 Nov 2022 • Thilina Ranbaduge, Dinusha Vatsalan, Ming Ding
The global model is then used by a linkage unit to distinguish unlabelled record pairs as matches and non-matches.
1 code implementation • 30 Oct 2022 • Zhuoyi Yang, Ming Ding, Yanhui Guo, Qingsong Lv, Jie Tang
In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain.
no code implementations • 20 Oct 2022 • Guangsheng Zhang, Bo Liu, Huan Tian, Tianqing Zhu, Ming Ding, Wanlei Zhou
As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale.
10 code implementations • 5 Oct 2022 • Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, WenGuang Chen, Peng Zhang, Yuxiao Dong, Jie Tang
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on Language Modelling on CLUE (OCNLI_50K)
1 code implementation • 21 Sep 2022 • Songjie Xie, Shuai Ma, Ming Ding, Yuanming Shi, Mingjian Tang, Youlong Wu
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver.
1 code implementation • 29 May 2022 • Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang
Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation.
Ranked #12 on Video Generation on UCF-101
1 code implementation • 28 May 2022 • Ziang Li, Ming Ding, Weikai Li, Zihan Wang, Ziyu Zeng, Yukuo Cen, Jie Tang
graph benchmark (IGB) consisting of 4 datasets.
1 code implementation • 28 Apr 2022 • Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang
The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images.
Ranked #44 on Text-to-Image Generation on MS COCO
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
no code implementations • 9 Feb 2022 • Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, Thilina Ranbaduge
As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.
no code implementations • 25 Jan 2022 • Peng Wang, Zihuai Lin, Xucun Yan, Zijiao Chen, Ming Ding, Yang song, Lu Meng
Cardiovascular disease has become one of the most significant threats endangering human life and health.
no code implementations • 25 Jan 2022 • Zijiao Chen, Zihuai Lin, Peng Wang, Ming Ding
With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing.
1 code implementation • 30 Dec 2021 • Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.
no code implementations • NeurIPS 2021 • Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
no code implementations • 16 Nov 2021 • Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
1 code implementation • 14 Oct 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Albert Y. Zomaya
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
no code implementations • 29 Sep 2021 • Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations.
no code implementations • 29 Sep 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
1 code implementation • ACL 2022 • Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
The few-shot natural language understanding (NLU) task has attracted much recent attention.
no code implementations • 11 Aug 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
no code implementations • 20 Jun 2021 • Kang Wei, Jun Li, Chuan Ma, Ming Ding, Cailian Chen, Shi Jin, Zhu Han, H. Vincent Poor
Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.
no code implementations • 31 May 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
no code implementations • NeurIPS 2021 • Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
4 code implementations • NeurIPS 2021 • Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.
Ranked #56 on Text-to-Image Generation on MS COCO (using extra training data)
no code implementations • NeurIPS 2021 • Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
1 code implementation • 10 May 2021 • Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, H. Vincent Poor
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
no code implementations • 16 Apr 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
1 code implementation • 25 Mar 2021 • David Smith, Frederik Geth, Elliott Vercoe, Andrew Feutrill, Ming Ding, Jonathan Chan, James Foster, Thierry Rakotoarivelo
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.
1 code implementation • 19 Mar 2021 • Xu Zou, Da Yin, Qingyang Zhong, Ming Ding, Hongxia Yang, Zhilin Yang, Jie Tang
To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation.
9 code implementations • ACL 2022 • Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Ranked #4 on Language Modelling on WikiText-103 (using extra training data)
7 code implementations • 18 Mar 2021 • Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
no code implementations • 12 Mar 2021 • Hanyu Xue, Bo Liu, Ming Ding, Tianqing Zhu, Dayong Ye, Li Song, Wanlei Zhou
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.
no code implementations • 2 Mar 2021 • Yunqian Wen, Li Song, Bo Liu, Ming Ding, Rong Xie
We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism.
no code implementations • 1 Mar 2021 • Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
no code implementations • 28 Jan 2021 • Kang Wei, Jun Li, Ming Ding, Chuan Ma, Yo-Seb Jeon, H. Vincent Poor
An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).
no code implementations • 18 Jan 2021 • Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, H. Vincent Poor
Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
1 code implementation • 1 Jan 2021 • Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
no code implementations • 22 Dec 2020 • Ming Ding, Zhiqi Chen, Jifu Li
In this paper, we introduce the notion of F-manifold color algebras and study their properties which extend some results for F-manifold algebras.
Rings and Algebras
no code implementations • 2 Dec 2020 • Jun Li, Yumeng Shao, Ming Ding, Chuan Ma, Kang Wei, Zhu Han, H. Vincent Poor
The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.
1 code implementation • NeurIPS 2020 • Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang
BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.
no code implementations • 24 Nov 2020 • Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin
The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.
no code implementations • 20 Sep 2020 • Chuan Ma, Jun Li, Ming Ding, Long Shi, Taotao Wang, Zhu Han, H. Vincent Poor
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
1 code implementation • 4 Jul 2020 • Chuan Ma, Jun Li, Ming Ding, Bo Liu, Kang Wei, Jian Weng, H. Vincent Poor
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.
4 code implementations • 17 Jun 2020 • Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
4 code implementations • 20 May 2020 • Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang
To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.
no code implementations • 29 Feb 2020 • Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, H. Vincent Poor
According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.
no code implementations • 1 Nov 2019 • Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farokhi Farhad, Shi Jin, Tony Q. S. Quek, H. Vincent Poor
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
no code implementations • 14 Sep 2019 • Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
no code implementations • 15 Aug 2019 • Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications.
1 code implementation • IJCNLP 2019 • Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.
Ranked #5 on Text Generation on ReDial
no code implementations • 15 Aug 2019 • Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem.
1 code implementation • 13 Jun 2019 • Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.
3 code implementations • ACL 2019 • Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Ranked #50 on Question Answering on HotpotQA
no code implementations • 24 Feb 2019 • Sina Shaham, Ming Ding, Bo Liu, Shuping Dang, Zihuai Lin, Jun Li
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
2 code implementations • 1 Sep 2018 • Ming Ding, Jie Tang, Jie Zhang
We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs.
1 code implementation • 7 Jun 2018 • Jie Zhang, Yan Wang, Jie Tang, Ming Ding
In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.
no code implementations • 16 May 2018 • Sina Shaham, Ming Ding, Bo Liu, Zihuai Lin, Jun Li
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.