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.
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.
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 • 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.
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.
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, 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.
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.
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.
3 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 #22 on
Text-to-Image Generation
on 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.
4 code implementations • 18 Mar 2021 • Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang
On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning.
1 code implementation • 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 #2 on
Language Modelling
on WikiText-103
no code implementations • 12 Mar 2021 • Bo Liu, Ming Ding, Hanyu Xue, 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.
2 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
2 code implementations • 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.
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.
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 #53 on
Question Answering
on HotpotQA
Multi-hop Question Answering
Multi-Hop Reading Comprehension
+1
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.
1 code implementation • 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.