Search Results for author: Jie Tang

Found 184 papers, 117 papers with code

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese

1 code implementation ACL 2022 Daniel Zhang-li, Jing Zhang, Jifan Yu, Xiaokang Zhang, Peng Zhang, Jie Tang, Juanzi Li

We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation.

Entity Linking Question Answering

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

no code implementations ACL 2022 Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, 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.

Language Modelling

Bilateral Propagation Network for Depth Completion

no code implementations17 Mar 2024 Jie Tang, Fei-Peng Tian, Boshi An, Jian Li, Ping Tan

Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image.

PST-Bench: Tracing and Benchmarking the Source of Publications

1 code implementation25 Feb 2024 Fanjin Zhang, Kun Cao, Yukuo Cen, Jifan Yu, Da Yin, Jie Tang

Tracing the source of research papers is a fundamental yet challenging task for researchers.

Benchmarking

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining

no code implementations24 Feb 2024 Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang

We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field.

Graph Mining

Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments

no code implementations22 Feb 2024 Yu Gu, Yiheng Shu, Hao Yu, Xiao Liu, Yuxiao Dong, Jie Tang, Jayanth Srinivasa, Hugo Latapie, Yu Su

The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist language agents capable of operating within complex real-world environments.

TriSampler: A Better Negative Sampling Principle for Dense Retrieval

no code implementations19 Feb 2024 Zhen Yang, Zhou Shao, Yuxiao Dong, Jie Tang

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance.

Retrieval

CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations

1 code implementation6 Feb 2024 Ji Qi, Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong, Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, Jie Tang

Vision-Language Models (VLMs) have demonstrated their widespread viability thanks to extensive training in aligning visual instructions to answers.

Visual Reasoning

Towards Efficient and Exact Optimization of Language Model Alignment

1 code implementation1 Feb 2024 Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang, Minlie Huang

We prove that EXO is guaranteed to optimize in the same direction as the RL algorithms asymptotically for arbitary parametrization of the policy, while enables efficient optimization by circumventing the complexities associated with RL algorithms.

Language Modelling Reinforcement Learning (RL)

LongAlign: A Recipe for Long Context Alignment of Large Language Models

1 code implementation31 Jan 2024 Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li

Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length.

Instruction Following

Sketch and Refine: Towards Fast and Accurate Lane Detection

no code implementations26 Jan 2024 Chao Chen, Jie Liu, Chang Zhou, Jie Tang, Gangshan Wu

At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers.

Lane Detection

SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning

1 code implementation15 Jan 2024 Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, Jie Tang

To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.

Math Mathematical Reasoning

APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding

no code implementations12 Jan 2024 Mingdao Liu, Aohan Zeng, Bowen Wang, Peng Zhang, Jie Tang, Yuxiao Dong

The massive adoption of large language models (LLMs) demands efficient deployment strategies.

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

no code implementations11 Jan 2024 Bo Chen, Xingyi Cheng, Pan Li, Yangli-ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song

We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework.

Protein Language Model

CogCartoon: Towards Practical Story Visualization

no code implementations17 Dec 2023 Zhongyang Zhu, Jie Tang

The state-of-the-art methods for story visualization demonstrate a significant demand for training data and storage, as well as limited flexibility in story presentation, thereby rendering them impractical for real-world applications.

Story Visualization

CogAgent: A Visual Language Model for GUI Agents

1 code implementation14 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.

Language Modelling Visual Question Answering

Intelligent Reflecting Surface-Aided Electromagnetic Stealth Against Radar Detection

no code implementations4 Dec 2023 Beixiong Zheng, Xue Xiong, Jie Tang, Rui Zhang

While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angle/direction range, making it challenging to ensure the expected stealth performance.

CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation

2 code implementations30 Nov 2023 Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

Since the natural language processing (NLP) community started to make large language models (LLMs), such as GPT-4, act as a critic to evaluate the quality of generated texts, most of them only train a critique generation model of a specific scale on specific datasets.

Language Modelling Large Language Model

Black-Box Prompt Optimization: Aligning Large Language Models without Model Training

1 code implementation7 Nov 2023 Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

However, these models are often not well aligned with human intents, which calls for additional treatments on them, that is, the alignment problem.

AgentTuning: Enabling Generalized Agent Abilities for LLMs

1 code implementation19 Oct 2023 Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, Jie Tang

Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities.

Memorization

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration

no code implementations16 Oct 2023 Jie Tang, Bin He, Junkai Xu, Tian Tan, Zhipeng Wang, Yanmin Zhou, Shuo Jiang

The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Design of Novel Loss Functions for Deep Learning in X-ray CT

no code implementations23 Sep 2023 Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare, Charles A. Bouman, Roman Melnyk, Jie Tang, Brian Nett

Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively.

Computed Tomography (CT)

GPT Can Solve Mathematical Problems Without a Calculator

1 code implementation6 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.

Language Modelling Math

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

1 code implementation28 Aug 2023 Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding.

Code Completion Few-Shot Learning

Robust Object Modeling for Visual Tracking

1 code implementation ICCV 2023 Yidong Cai, Jie Liu, Jie Tang, Gangshan Wu

To enjoy the merits of both methods, we propose a robust object modeling framework for visual tracking (ROMTrack), which simultaneously models the inherent template and the hybrid template features.

Object Visual Tracking

AgentBench: Evaluating LLMs as Agents

1 code implementation7 Aug 2023 Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang

We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.

Decision Making Instruction Following

Lightweight Super-Resolution Head for Human Pose Estimation

1 code implementation31 Jul 2023 Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu

We first propose the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing.

Pose Estimation Quantization +1

MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge

1 code implementation27 Jun 2023 Yuanxi Sun, Rui He, Youzeng Li, Zuwei Huang, Feng Hu, Xu Cheng, Jie Tang

The Generic Event Boundary Detection (GEBD) task aims to build a model for segmenting videos into segments by detecting general event boundaries applicable to various classes.

Boundary Detection Generic Event Boundary Detection +2

Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method

1 code implementation11 Jun 2023 Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Shu Zhao, Peng Zhang, Jie Tang

To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance.

Knowledge Distillation Language Modelling

GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model

1 code implementation11 Jun 2023 Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Yang Yang, Hongyin Tang, Keqing He, Jiahao Liu, Jingang Wang, Shu Zhao, Peng Zhang, Jie Tang

Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices.

General Knowledge Knowledge Distillation +1

Optimizing Airbnb Search Journey with Multi-task Learning

no code implementations28 May 2023 Chun How Tan, Austin Chan, Malay Haldar, Jie Tang, Xin Liu, Mustafa Abdool, Huiji Gao, Liwei He, Sanjeev Katariya

The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking.

Multi-Task Learning

Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration

no code implementations24 May 2023 Kejuan Yang, Xiao Liu, Kaiwen Men, Aohan Zeng, Yuxiao Dong, Jie Tang

We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e. g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.

Video Frame Interpolation with Densely Queried Bilateral Correlation

1 code implementation26 Apr 2023 Chang Zhou, Jie Liu, Jie Tang, Gangshan Wu

To better model correlations and to produce more accurate motion fields, we propose the Densely Queried Bilateral Correlation (DQBC) that gets rid of the receptive field dependency problem and thus is more friendly to small and fast-moving objects.

Motion Estimation Video Frame Interpolation

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

2 code implementations10 Apr 2023 Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.

Self-Supervised Learning

MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs

1 code implementation5 Apr 2023 Jifan Yu, Mengying Lu, Qingyang Zhong, Zijun Yao, Shangqing Tu, Zhengshan Liao, Xiaoya Li, Manli Li, Lei Hou, Hai-Tao Zheng, Juanzi Li, Jie Tang

Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education.

cognitive diagnosis Knowledge Tracing

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X

2 code implementations30 Mar 2023 Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.

Code Generation

GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation

1 code implementation26 Mar 2023 Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Yuxiao Dong, Bin Xu, Lei Hou, Juanzi Li, Jie Tang, Weidong Guo, Hui Liu, Yu Xu

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.

Video Captioning

GPT-4 Technical Report

9 code implementations Preprint 2023 OpenAI, :, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Jan Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, Barret Zoph

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.

 Ranked #1 on Only Connect Walls Dataset Task 1 (Grouping) on OCW (using extra training data)

Arithmetic Reasoning Bug fixing +9

GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation

1 code implementation28 Feb 2023 Jing Zhang, Xiaokang Zhang, Daniel Zhang-li, Jifan Yu, Zijun Yao, Zeyao Ma, Yiqi Xu, Haohua Wang, Xiaohan Zhang, Nianyi Lin, Sunrui Lu, Juanzi Li, Jie Tang

We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge.

Dialogue Evaluation Dialogue Generation +2

Scaling laws for single-agent reinforcement learning

no code implementations31 Jan 2023 Jacob Hilton, Jie Tang, John Schulman

Recent work has shown that, in generative modeling, cross-entropy loss improves smoothly with model size and training compute, following a power law plus constant scaling law.

reinforcement-learning Reinforcement Learning (RL)

From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution

1 code implementation30 Nov 2022 Jie Liu, Chao Chen, Jie Tang, Gangshan Wu

In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a $3\times3$ convolution is applied to process the finest details.

Image Super-Resolution

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments

no code implementations21 Nov 2022 Tim Tianyi Yang, Tom Tianze Yang, Andrew Liu, Jie Tang, Na An, Shaoshan Liu, Xue Liu

Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.

Parameter-Efficient Tuning Makes a Good Classification Head

1 code implementation30 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.

Classification Natural Language Understanding

Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries

1 code implementation16 Aug 2022 Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang

In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.

Towards a General Pre-training Framework for Adaptive Learning in MOOCs

1 code implementation18 Jul 2022 Qingyang Zhong, Jifan Yu, Zheyuan Zhang, Yiming Mao, Yuquan Wang, Yankai Lin, Lei Hou, Juanzi Li, Jie Tang

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations.

Knowledge Tracing

Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers

2 code implementations14 Jul 2022 Weng Lam Tam, Xiao Liu, Kaixuan Ji, Lilong Xue, Xingjian Zhang, Yuxiao Dong, Jiahua Liu, Maodi Hu, Jie Tang

By updating only 0. 1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated.

Retrieval Text Retrieval +1

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

2 code implementations23 Jun 2022 Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.

Imitation Learning reinforcement-learning +1

GACT: Activation Compressed Training for Generic Network Architectures

1 code implementation22 Jun 2022 Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael Mahoney, Alvin Cheung

Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.

CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

1 code implementation29 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.

Text-to-Video Generation Video Generation

GraphMAE: Self-Supervised Masked Graph Autoencoders

3 code implementations22 May 2022 Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang

Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.

Contrastive Learning Graph Classification +4

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

1 code implementation28 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.

Language Modelling Super-Resolution +1

Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

1 code implementation18 Apr 2022 Zongcai Du, Ding Liu, Jie Liu, Jie Tang, Gangshan Wu, Lean Fu

Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.

Image Super-Resolution

WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models

no code implementations22 Mar 2022 Sha Yuan, Shuai Zhao, Jiahong Leng, Zhao Xue, Hanyu Zhao, Peiyu Liu, Zheng Gong, Wayne Xin Zhao, Junyi Li, Jie Tang

The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.

Image Captioning Question Answering +2

GRAND+: Scalable Graph Random Neural Networks

1 code implementation12 Mar 2022 Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang

In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.

Data Augmentation Graph Learning +2

Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

no code implementations8 Mar 2022 Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, YuTing Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, Jie Tang

Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise.

Graph Attention reinforcement-learning +1

SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

1 code implementation2 Mar 2022 Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang

We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.

Entity Alignment Knowledge Graphs +1

Training Free Graph Neural Networks for Graph Matching

1 code implementation14 Jan 2022 Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua

We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).

Entity Alignment Graph Matching +1

BodyGAN: General-Purpose Controllable Neural Human Body Generation

no code implementations CVPR 2022 Chaojie Yang, Hanhui Li, Shengjie Wu, Shengkai Zhang, Haonan Yan, Nianhong Jiao, Jie Tang, Runnan Zhou, Xiaodan Liang, Tianxiang Zheng

This is because current methods mainly rely on a single pose/appearance model, which is limited in disentangling various poses and appearance in human images.

Disentanglement Image Generation +1

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation30 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.

Benchmarking

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.

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

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

Calculating Question Similarity is Enough: A New Method for KBQA Tasks

no code implementations15 Nov 2021 Hanyu Zhao, Sha Yuan, Jiahong Leng, Xiang Pan, Guoqiang Wang, Ledell Wu, Jie Tang

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base.

Entity Linking Knowledge Base Question Answering +3

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 Benchmarking +1

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

4 code implementations14 Oct 2021 Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, 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.

Language Modelling

Graph Contrastive Learning for Anomaly Detection

2 code implementations17 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 Binary Classification +2

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

2 code implementations3 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

1 code implementation17 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.

Adversarial Attack

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 via Non-Autoregressive Generative Transformers

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

CogView: Mastering Text-to-Image Generation via Transformers

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)

Super-Resolution Zero-Shot Text-to-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

3 code implementations20 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

3 code implementations24 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

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

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)

Abstractive Text Summarization Classification +4

GPT Understands, Too

7 code implementations18 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).

Knowledge Probing Language Modelling +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

OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

1 code implementation3 Mar 2021 Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang

Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.

Language Modelling Link Prediction

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

CogDL: A Comprehensive Library for Graph Deep Learning

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

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

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.

Clustering

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

2 code implementations14 Dec 2020 Bo Chen, Jing Zhang, Xiaokang Zhang, Xiaobin Tang, Lingfan Cai, Hong Chen, Cuiping Li, Peng Zhang, Jie Tang

In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.

Active Learning Contrastive Learning +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 Text Classification

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

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

no code implementations15 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

4 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.

Vocal Bursts Intensity Prediction

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 Novel Concepts

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

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

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

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.

Recommendation Systems Text Generation

Learning Guided Convolutional Network for Depth Completion

2 code implementations3 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 Virtual 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 a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.

Experimental Design Marketing

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 +1

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 Thompson Sampling

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

2 code implementations16 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 +1

Semi-supervised Learning on Graphs with Generative Adversarial Nets

2 code implementations1 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?

Relation 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.

Cloud Computing Memorization

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).

Management

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.

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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