787 citations · 3.8k across the 54 of their papers we have counts for
54 papers
MBIR Training for a 2.5D DL network in X-ray CT
Obaidullah Rahman, Madhuri Nagare, Ken D. Sauer +4
In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection te…
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
Shicheng Tan, Weng Lam Tam, Yuanchun Wang +9
Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment…
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method
Shicheng Tan, Weng Lam Tam, Yuanchun Wang +4
The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly…
Towards Lightweight and Automated Representation Learning System for Networks
Yuyang Xie, Jiezhong Qiu, Laxman Dhulipala +4
We propose LIGHTNE 2.0, a cost-effective, scalable, automated, and high-quality network embedding system that scales to graphs with hundreds of billions of edges on a single machin…
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit
Bo Chen, Jing Zhang, Fanjin Zhang +5
Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, a…
Network representation learning: A macro and micro view
Xueyi Liu, Jie Tang
Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can b…
Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact
Yinyu Jin, Sha Yuan, Zhou Shao +2
The Turing Award is recognized as the most influential and prestigious award in the field of computer science(CS). With the rise of the science of science (SciSci), a large amount…
Understanding WeChat User Preferences and "Wow" Diffusion
Fanjin Zhang, Jie Tang, Xueyi Liu +9
WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. "Top Stories" is a novel friend-enhanced recommendation engine in WeChat, i…
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong +5
Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent develop…
Understanding Negative Sampling in Graph Representation Learning
Zhen Yang, Ming Ding, Chang Zhou +3
Graph representation learning has been extensively studied in recent years. Despite its potential in generating continuous embeddings for various networks, both the effectiveness a…
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
Shen Wang, Jibing Gong, Jinlong Wang +4
Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attra…
Attention: to Better Stand on the Shoulders of Giants
Sha Yuan, Zhou Shao, Yu Zhang +4
Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availabil…
Graph Random Neural Network for Semi-Supervised Learning on Graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong +6
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer fro…
Controllable Multi-Interest Framework for Recommendation
Yukuo Cen, Jianwei Zhang, Xu Zou +3
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequent…
Modelling High-Order Social Relations for Item Recommendation
Yang Liu, Liang Chen, Xiangnan He +3
The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relati…
A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Dong Zhang, Shu Zhao, Zhen Duan +3
Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the su…
Blockwise Self-Attention for Long Document Understanding
Jiezhong Qiu, Hao Ma, Omer Levy +3
We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the…
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
Jifan Yu, Chenyu Wang, Gan Luo +4
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drift…
Towards Knowledge-Based Recommender Dialog System
Qibin Chen, Junyang Lin, Yichang Zhang +4
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog…
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation
Xichen Ding, Jie Tang, Tracy Liu +5
Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from t…
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Jiezhong Qiu, Yuxiao Dong, Hao Ma +4
We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network e…
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ming Ding, Chang Zhou, Qibin Chen +2
We propose a new CogQA framework for multi-hop question answering in web-scale documents. Inspired by the dual process theory in cognitive science, the framework gradually builds a…
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang +2
Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommend…
Representation Learning for Attributed Multiplex Heterogeneous Network
Yukuo Cen, Xu Zou, Jianwei Zhang +3
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and ca…