Search Results for author: Jie Tang

Found 100 papers, 57 papers with code

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

no code implementations NeurIPS 2021 Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further.

Hierarchical Reinforcement Learning

Adaptive Diffusion in Graph Neural Networks

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.

AdaDM: Enabling Normalization for Image Super-Resolution

1 code implementation27 Nov 2021 Jie Liu, Jie Tang, Gangshan Wu

We found that the standard deviation of the residual feature shrinks a lot after normalization layers, which causes the performance degradation in SR networks.

Image Super-Resolution

Network representation learning: A macro and micro view

no code implementations21 Nov 2021 Xueyi Liu, Jie Tang

Representation learning can facilitate the design of new algorithms on the graph data.

Network Embedding

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

1 code implementation8 Nov 2021 Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang

To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.

Adversarial Robustness

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

2 code implementations14 Oct 2021 Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, Jie Tang

Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.

Fine-tuning Language Modelling

GCCAD: Graph Contrastive Coding for Anomaly Detection

1 code implementation17 Aug 2021 Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang

To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.

Anomaly Detection Feature Engineering +1

Modeling Protein Using Large-scale Pretrain Language Model

1 code implementation17 Aug 2021 Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, Jie Tang

The emergence of deep learning models makes modeling data patterns in large quantities of data possible.

Drug Discovery Language Modelling

EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training

1 code implementation3 Aug 2021 Hao Zhou, Pei Ke, Zheng Zhang, Yuxian Gu, Yinhe Zheng, Chujie Zheng, Yida Wang, Chen Henry Wu, Hao Sun, Xiaocong Yang, Bosi Wen, Xiaoyan Zhu, Minlie Huang, Jie Tang

Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.

Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact

no code implementations22 Jun 2021 Yinyu Jin, Sha Yuan, Zhou Shao, Wendy Hall, Jie Tang

The Turing Award is recognized as the most influential and prestigious award in the field of computer science(CS).

Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions

no code implementations19 Jun 2021 Yushan Liu, Shun Zhang, Feifei Gao, Jie Tang, Octavia A. Dobre

Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications.

A Self-supervised Method for Entity Alignment

no code implementations17 Jun 2021 Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang

We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.

Contrastive Learning Entity Alignment +2

TDGIA:Effective Injection Attacks on Graph Neural Networks

1 code implementation12 Jun 2021 Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang

In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.

A Generalizable Approach to Learning Optimizers

1 code implementation2 Jun 2021 Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba

A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.

Language Modelling

M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis

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.

Image Generation

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

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.

Image Generation

Anchor-based Plain Net for Mobile Image Super-Resolution

1 code implementation20 May 2021 Zongcai Du, Jie Liu, Jie Tang, Gangshan Wu

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward.

Image Super-Resolution Quantization

FastMoE: A Fast Mixture-of-Expert Training System

1 code implementation24 Mar 2021 Jiaao He, Jiezhong Qiu, Aohan Zeng, Zhilin Yang, Jidong Zhai, Jie Tang

However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system.

Language Modelling

All NLP Tasks Are Generation Tasks: A General Pretraining Framework

1 code implementation18 Mar 2021 Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

On the other hand, NLP tasks are different in nature, with three main categories being classification, unconditional generation, and conditional generation.

Abstractive Text Summarization Classification +5

GPT Understands, Too

4 code implementations18 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.

Fine-tuning LAMA +2

Understanding WeChat User Preferences and "Wow" Diffusion

1 code implementation4 Mar 2021 Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu

"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.

Graph Representation Learning Social and Information Networks

CogDL: Toolkit for Deep Learning on Graphs

1 code implementation1 Mar 2021 Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, graph classification, etc.

Graph Classification Graph Embedding +5

M6: A Chinese Multimodal Pretrainer

no code implementations1 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.

Image Generation

Generalizing Graph Convolutional Networks

1 code implementation1 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.

Local Clustering Graph Neural Networks

no code implementations1 Jan 2021 Jiezhong Qiu, Yukuo Cen, Qibin Chen, Chang Zhou, Jingren Zhou, Hongxia Yang, Jie Tang

Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.

COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

1 code implementation14 Dec 2020 Bo Chen, Jing Zhang, Xiaokang Zhang, Xiaobin Tang, Lingfan Cai, Hong Chen, Cuiping Li, Peng Zhang, Jie Tang

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants, collaborators, and candidate qualifications.

Contrastive Learning Domain Adaptation +2

Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines

no code implementations2 Dec 2020 Yiming Gan, Yu Bo, Boyuan Tian, Leimeng Xu, Wei Hu, Shaoshan Liu, Qiang Liu, Yanjun Zhang, Jie Tang, Yuhao Zhu

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe.

Self-Driving Cars Hardware Architecture

CogLTX: Applying BERT to Long Texts

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.

Text Classification

ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu

Within the prosperity of Massive Open Online Courses (MOOCs), the education applications that automatically provide extracurricular knowledge for MOOC users become rising research topics.

Hierarchical Reinforcement Learning

Residual Feature Distillation Network for Lightweight Image Super-Resolution

2 code implementations24 Sep 2020 Jie Liu, Jie Tang, Gangshan Wu

Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN).

Image Super-Resolution

A Survey of FPGA-Based Robotic Computing

no code implementations13 Sep 2020 Zishen Wan, Bo Yu, Thomas Yuang Li, Jie Tang, Yuhao Zhu, Yu Wang, Arijit Raychowdhury, Shaoshan Liu

On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.

Autonomous Vehicles Legged Robots

A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices

no code implementations NeurIPS 2020 Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang

Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.

Graph Learning Graph Representation Learning

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs

no code implementations ACL 2020 Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e. g., course concept extraction, prerequisite relation discovery, etc.

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations23 Jun 2020 Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

Representation Learning

Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

no code implementations19 Jun 2020 Haitham Al-Obiedollah, Kanapathippillai Cumanan, Jeyarajan Thiyagalingam, Jie Tang, Alister G. Burr, Zhiguo Ding, Octavia A. Dobre

In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics.

Self-supervised Learning: Generative or Contrastive

1 code implementation15 Jun 2020 Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, Jie Tang

As an alternative, self-supervised learning attracts many researchers for its soaring performance on representation learning in the last several years.

Graph Learning Representation Learning +1

Attention: to Better Stand on the Shoulders of Giants

no code implementations27 May 2020 Sha Yuan, Zhou Shao, Yu Zhang, Xingxing Wei, Tong Xiao, Yifan Wang, Jie Tang

In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research.

Understanding Negative Sampling in Graph Representation Learning

2 code implementations20 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.

Graph Learning Graph Representation Learning +2

Modelling High-Order Social Relations for Item Recommendation

no code implementations23 Mar 2020 Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang

The prevalence of online social network makes it compulsory to study how social relations affect user choice.

Simple and Lightweight Human Pose Estimation

1 code implementation23 Nov 2019 Zhe Zhang, Jie Tang, Gangshan Wu

Specifically, our LPN-50 can achieve 68. 7 in AP score on the COCO test-dev set, with only 2. 7M parameters and 1. 0 GFLOPs, while the inference speed is 17 FPS on an Intel i7-8700K CPU machine.

Keypoint Detection

Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation

no code implementations25 Sep 2019 Jie Zhang, Yuxiao Dong, Jie Tang

In this paper, we revisit the mathematical foundation of GCNs and study how to extend their representation capacity.

Graph Attention Graph Classification +1

Dimensional Reweighting Graph Convolution Networks

no code implementations25 Sep 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Zijun Yao, Hongxia Yang, Jie Tang

In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.

Node Classification

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

no code implementations ACL 2019 Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Zhiyuan Liu

As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users.

Towards Knowledge-Based Recommender Dialog System

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.

Recommendation Systems

Learning Guided Convolutional Network for Depth Completion

1 code implementation3 Aug 2019 Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, Ping Tan

It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.

Autonomous Driving Depth Completion +1

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

1 code implementation8 Jul 2019 Xichen Ding, Jie Tang, Tracy Liu, Cheng Xu, Yaping Zhang, Feng Shi, Qixia Jiang, Dan Shen

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei.

Dimensional Reweighting Graph Convolutional Networks

2 code implementations4 Jul 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Hongxia Yang, Jie Tang

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.

Node Classification

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

1 code implementation26 Jun 2019 Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang

Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.

Network Embedding

Gift Contagion in Online Groups: Evidence From WeChat Red Packets

no code implementations24 Jun 2019 Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang

Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.

Experimental Design

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

1 code implementation22 Jun 2019 Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

1 code implementation13 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.

Knowledge Graphs Relational Reasoning

Sequential Scenario-Specific Meta Learner for Online Recommendation

1 code implementation2 Jun 2019 Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang

Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.

Few-Shot Learning

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

4 code implementations29 Mar 2019 Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang

In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.

Text Generation

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

1 code implementation20 Feb 2019 Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua

Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.

General Classification Node Classification

Bandit Learning with Implicit Feedback

1 code implementation NeurIPS 2018 Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun

Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output.

Bayesian Inference

Modeling and Predicting Citation Count via Recurrent Neural Network with Long Short-Term Memory

no code implementations6 Nov 2018 Sha Yuan, Jie Tang, Yu Zhang, Yifan Wang, Tong Xiao

The rapid evolution of scientific research has been creating a huge volume of publications every year.

Digital Libraries Physics and Society

Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism

no code implementations6 Nov 2018 Sha Yuan, Yu Zhang, Jie Tang, Hua-Wei Shen, Xingxing Wei

Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity.

Fast Randomized PCA for Sparse Data

1 code implementation16 Oct 2018 Xu Feng, Yuyang Xie, Mingye Song, Wenjian Yu, Jie Tang

The algorithm has similar accuracy to the basic randomized SVD (rPCA) algorithm (Halko et al., 2011), but is largely optimized for sparse data.

Dimensionality Reduction Information Retrieval

Semi-supervised Learning on Graphs with Generative Adversarial Nets

1 code implementation1 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.

DeepInf: Social Influence Prediction with Deep Learning

1 code implementation15 Jul 2018 Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang

Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.

Feature Engineering Representation Learning

Spectral Network Embedding: A Fast and Scalable Method via Sparsity

1 code implementation7 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.

Link Prediction Network Embedding +1

Expert Finding in Community Question Answering: A Review

no code implementations21 Apr 2018 Sha Yuan, Yu Zhang, Jie Tang, Juan Bautista Cabotà

Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance.

Community Question Answering Ensemble Learning +2

Teaching Autonomous Driving Using a Modular and Integrated Approach

no code implementations22 Feb 2018 Jie Tang, Shaoshan Liu, Songwen Pei, Stephane Zuckerman, Chen Liu, Weisong Shi, Jean-Luc Gaudiot

Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other.

Autonomous Driving

Revisiting Knowledge Base Embedding as Tensor Decomposition

no code implementations ICLR 2018 Jiezhong Qiu, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang

We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.

Link Prediction Tensor Decomposition

Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation

no code implementations IJCNLP 2017 Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li, Jie Tang

Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education.

Fast Top-k Area Topics Extraction with Knowledge Base

no code implementations13 Oct 2017 Fang Zhang, Xiaochen Wang, Jingfei Han, Jie Tang, Shiyin Wang, Marie-Francine Moens

We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas.

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

4 code implementations9 Oct 2017 Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang

This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.

Network Embedding

Prerequisite Relation Learning for Concepts in MOOCs

no code implementations ACL 2017 Liangming Pan, Chengjiang Li, Juanzi Li, Jie Tang

What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares?

Representation Learning

Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm

no code implementations16 Apr 2017 Shaoshan Liu, Bolin Ding, Jie Tang, Dawei Sun, Zhe Zhang, Grace Tsai, Jean-Luc Gaudiot

The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data.

A Probabilistic Framework for Location Inference from Social Media

no code implementations23 Feb 2017 Yujie Qian, Jie Tang, Zhilin Yang, Binxuan Huang, Wei Wei, Kathleen M. Carley

In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).

Weakly Learning to Match Experts in Online Community

no code implementations14 Nov 2016 Yujie Qian, Jie Tang, Kan Wu

The challenge is how to trade off the matching degree between users' expertise and the question topic, and the likelihood of positive response from the invited users.

OpenAI Gym

45 code implementations5 Jun 2016 Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba

OpenAI Gym is a toolkit for reinforcement learning research.

An Empirical Study on Academic Commentary and Its Implications on Reading and Writing

no code implementations12 Feb 2016 Tai Wang, Xiangen Hu, Keith Shubeck, Zhiqiang Cai, Jie Tang

The relationship between reading and writing (RRW) is one of the major themes in learning science.

Word Embedding based Correlation Model for Question/Answer Matching

no code implementations15 Nov 2015 Yikang Shen, Wenge Rong, Nan Jiang, Baolin Peng, Jie Tang, Zhang Xiong

With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web.

Question Answering Translation

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

no code implementations4 Aug 2015 Zhilin Yang, Jie Tang, William Cohen

GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities---i. e., social network users and knowledge concepts---in a shared latent topic space.

Knowledge Graphs

Panther: Fast Top-k Similarity Search in Large Networks

2 code implementations10 Apr 2015 Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li

The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.

Social and Information Networks

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