Search Results for author: Lingfei Wu

Found 112 papers, 52 papers with code

Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport

1 code implementation EMNLP 2021 Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji, Kathleen McKeown

Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.

Timeline Summarization

Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference

1 code implementation6 Jul 2024 Kai Shen, Lingfei Wu, Siliang Tang, Fangli Xu, Bo Long, Yueting Zhuang, Jian Pei

The visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e. g. answer type).

Graph-to-Sequence Implicit Relations +2

KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs

1 code implementation6 Mar 2024 Ruoqi Liu, Lingfei Wu, Ping Zhang

To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE.

Knowledge Graphs

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection

no code implementations13 Nov 2023 Yangkai Du, Tengfei Ma, Lingfei Wu, Xuhong Zhang, Shouling Ji

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention.

Clone Detection Contrastive Learning

CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code

1 code implementation24 Oct 2023 Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, Wenhai Wang

Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language.

Code Summarization

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

no code implementations7 Jun 2023 Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Hongshen Chen, Zhuozhi Ding, Sulong Xu, Lingfei Wu

In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).

Decision Making Recommendation Systems

Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization

1 code implementation18 May 2023 Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, Wenhai Wang

In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries.

Code Summarization Decoder +3

SkillQG: Learning to Generate Question for Reading Comprehension Assessment

no code implementations8 May 2023 Xiaoqiang Wang, Bang Liu, Siliang Tang, Lingfei Wu

We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models.

Machine Reading Comprehension Question Answering +2

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

no code implementations22 Feb 2023 Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu

Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.

Graph Neural Network

Pruning Before Training May Improve Generalization, Provably

no code implementations1 Jan 2023 Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang

It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance.

Network Pruning

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

no code implementations24 Dec 2022 Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, Lingfei Wu

A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

no code implementations1 Dec 2022 Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, Yanjie Fu

The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.

Automatic Scene-based Topic Channel Construction System for E-Commerce

no code implementations6 Oct 2022 Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding, Bo Long

To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.

Clustering Marketing

Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search

no code implementations4 Oct 2022 Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang

In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.

Knowledge Distillation

Automatic Generation of Product-Image Sequence in E-commerce

1 code implementation26 Jun 2022 Xiaochuan Fan, Chi Zhang, Yong Yang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long, Lingfei Wu

For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.

Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing

no code implementations22 Jun 2022 Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu

Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold.

Fairness

Automatic Controllable Product Copywriting for E-Commerce

1 code implementation21 Jun 2022 Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.

Aspect Extraction Language Modelling +2

TeKo: Text-Rich Graph Neural Networks with External Knowledge

no code implementations15 Jun 2022 Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).

Graph Neural Network

Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

1 code implementation4 Jun 2022 Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang

Moreover, when handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise on a corrupted dataset.

Few-Shot Learning

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

no code implementations25 May 2022 Cuiying Huo, Di Jin, Chundong Liang, Dongxiao He, Tie Qiu, Lingfei Wu

In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation.

Graph Neural Network Recommendation Systems

Meta Policy Learning for Cold-Start Conversational Recommendation

1 code implementation24 May 2022 Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu

We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.

Meta Reinforcement Learning Recommendation Systems +2

Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce

no code implementations21 May 2022 Xueying Zhang, Kai Shen, Chi Zhang, Xiaochuan Fan, Yun Xiao, Zhen He, Bo Long, Lingfei Wu

In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.

Attribute Language Modelling

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

no code implementations30 Apr 2022 Cuiying Huo, Dongxiao He, Yawen Li, Di Jin, Jianwu Dang, Weixiong Zhang, Witold Pedrycz, Lingfei Wu

However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.

Attribute Contrastive Learning +1

QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance

no code implementations29 Apr 2022 Xiaoqiang Wang, Bang Liu, Siliang Tang, Lingfei Wu

Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts.

Question Generation Question-Generation +1

Feeding What You Need by Understanding What You Learned

no code implementations ACL 2022 Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang, Lingfei Wu

In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.

Machine Reading Comprehension

Sequential Search with Off-Policy Reinforcement Learning

no code implementations1 Feb 2022 Dadong Miao, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu, Yunjiang Jiang

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.

reinforcement-learning Reinforcement Learning (RL) +1

Compact Graph Structure Learning via Mutual Information Compression

2 code implementations14 Jan 2022 Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, Chuan Shi

Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.

Graph structure learning

Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art

1 code implementation23 Dec 2021 Xiang Ling, Lingfei Wu, Jiangyu Zhang, Zhenqing Qu, Wei Deng, Xiang Chen, Yaguan Qian, Chunming Wu, Shouling Ji, Tianyue Luo, Jingzheng Wu, Yanjun Wu

Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.

Adversarial Attack Malware Detection +2

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

1 code implementation22 Dec 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei

As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.

Attribute Multiple-choice

Automatic Product Copywriting for E-Commerce

no code implementations15 Dec 2021 Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu, Lingfei Wu

It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.

Product Recommendation Text Generation

Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory

no code implementations NeurIPS 2021 Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou

Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.

Network Pruning

Learning to Generate Visual Questions with Noisy Supervision

1 code implementation NeurIPS 2021 Shen Kai, Lingfei Wu, Siliang Tang, Yueting Zhuang, Zhen He, Zhuoye Ding, Yun Xiao, Bo Long

The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).

Question Generation Question-Generation +1

Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Hongyun Zhang, Zhihua Wei, Po Hu, Fangli Xu, Bo Long

Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.

reinforcement-learning Reinforcement Learning (RL) +1

Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long

Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.

Answer Selection Graph Question Answering +3

Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

no code implementations24 Sep 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long

In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.

Graph Learning Graph Neural Network

Multi-behavior Graph Contextual Aware Network for Session-based Recommendation

no code implementations24 Sep 2021 Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long

Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).

Session-Based Recommendations

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

1 code implementation8 Jul 2021 Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei

Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.

Graph Neural Network Session-Based Recommendations

Graph Neural Networks for Natural Language Processing: A Survey

1 code implementation10 Jun 2021 Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).

Decoder graph construction +1

Deep Learning on Graphs for Natural Language Processing

no code implementations NAACL 2021 Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.

graph construction Graph Representation Learning +10

Relation-aware Graph Attention Model With Adaptive Self-adversarial Training

no code implementations14 Feb 2021 Xiao Qin, Nasrullah Sheikh, Berthold Reinwald, Lingfei Wu

Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training.

Attribute Entity Embeddings +3

Low-skilled Occupations Face the Highest Upskilling Pressure

1 code implementation27 Jan 2021 Di Tong, Lingfei Wu, James Allen Evans

Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs.

A Neural Question Answering System for Basic Questions about Subroutines

no code implementations11 Jan 2021 Aakash Bansal, Zachary Eberhart, Lingfei Wu, Collin McMillan

In this paper, we take initial steps to bringing state-of-the-art neural QA technologies to Software Engineering applications by designing a context-based QA system for basic questions about subroutines.

Decoder Question Answering

Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning

no code implementations1 Jan 2021 Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.

Graph Classification Graph Matching +2

Differentiable Graph Optimization for Neural Architecture Search

no code implementations1 Jan 2021 Chengyue Huang, Lingfei Wu, Yadong Ding, Siliang Tang, Fangli Xu, Chang Zong, Chilie Tan, Yueting Zhuang

To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.

Bayesian Optimization Graph Neural Network +1

Robust Meta-learning with Noise via Eigen-Reptile

no code implementations1 Jan 2021 Dong Chen, Lingfei Wu, Siliang Tang, Fangli Xu, Juncheng Li, Chang Zong, Chilie Tan, Yueting Zhuang

In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.

Few-Shot Learning

Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference

no code implementations1 Jan 2021 Shen Kai, Lingfei Wu, Siliang Tang, Fangli Xu, Zhu Zhang, Yu Qiang, Yueting Zhuang

The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e. g. answer type or the answer itself).

Graph-to-Sequence Question Generation +1

Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

no code implementations25 Oct 2020 Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji

Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks.

Graph Neural Network

Deep Graph Matching and Searching for Semantic Code Retrieval

no code implementations24 Oct 2020 Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.

Graph Matching Retrieval

Stronger Transformers for Neural Multi-Hop Question Generation

no code implementations22 Oct 2020 Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton

In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.

Question Generation Question-Generation

Technical Question Answering across Tasks and Domains

1 code implementation NAACL 2021 Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang

In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.

Question Answering Reading Comprehension +2

Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning

1 code implementation EMNLP 2020 Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, Shouling Ji

Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.

Contrastive Learning Document Summarization +1

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

1 code implementation8 Jul 2020 Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.

Graph Classification Graph Matching +4

A Joint Neural Model for Information Extraction with Global Features

no code implementations ACL 2020 Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.

Decoder Sentence

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

2 code implementations NeurIPS 2020 Yu Chen, Lingfei Wu, Mohammed J. Zaki

In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding.

Graph Embedding Graph Learning +1

Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement

1 code implementation9 Jun 2020 Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye

Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.

Disentanglement Graph Generation

Crossing Variational Autoencoders for Answer Retrieval

no code implementations ACL 2020 Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang

Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.

Retrieval

A Multi-Perspective Architecture for Semantic Code Search

no code implementations ACL 2020 Rajarshi Haldar, Lingfei Wu, JinJun Xiong, Julia Hockenmaier

The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.

Code Search Text Matching

Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

1 code implementation ACL 2020 Luyang Huang, Lingfei Wu, Lu Wang

Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive.

Abstractive Text Summarization Cloze Test +1

Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks

1 code implementation13 Apr 2020 Yu Chen, Lingfei Wu, Mohammed J. Zaki

In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.

Data Augmentation Decoder +4

Improved Automatic Summarization of Subroutines via Attention to File Context

1 code implementation10 Apr 2020 Sakib Haque, Alexander LeClair, Lingfei Wu, Collin McMillan

In this paper, we present an approach that models the file context of subroutines (i. e. other subroutines in the same file) and uses an attention mechanism to find words and concepts to use in summaries.

Software Engineering

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

no code implementations27 Feb 2020 Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.

Image Classification Natural Language Understanding +1

Deep Iterative and Adaptive Learning for Graph Neural Networks

1 code implementation17 Dec 2019 Yu Chen, Lingfei Wu, Mohammed J. Zaki

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.

Graph Learning Graph structure learning +2

Efficient Global String Kernel with Random Features: Beyond Counting Substructures

no code implementations25 Nov 2019 Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal

In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.

KerGM: Kernelized Graph Matching

1 code implementation NeurIPS 2019 Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai

Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.

Graph Matching

Graph Enhanced Cross-Domain Text-to-SQL Generation

no code implementations WS 2019 Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock

Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.

Natural Language Understanding Semantic Parsing +3

MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics

no code implementations5 Oct 2019 Fangli Xu, Lingfei Wu, KP Thai, Carol Hsu, Wei Wang, Richard Tong

Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement.

EEG

Iterative Deep Graph Learning for Graph Neural Networks

no code implementations25 Sep 2019 Yu Chen, Lingfei Wu, Mohammed J. Zaki

In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously.

Graph Embedding Graph Learning +2

Hierarchical Graph Matching Networks for Deep Graph Similarity Learning

no code implementations25 Sep 2019 Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu, Shouling Ji

The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.

Graph Matching Graph Neural Network +1

Multi-stage Deep Classifier Cascades for Open World Recognition

1 code implementation26 Aug 2019 Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.

Object Recognition

DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

no code implementations22 Aug 2019 Yuyang Gao, Lingfei Wu, Houman Homayoun, Liang Zhao

In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.

Decoder Graph-to-Sequence

GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

1 code implementation31 Jul 2019 Yu Chen, Lingfei Wu, Mohammed J. Zaki

The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.

Graph Neural Network Graph structure learning +1

Attacking Graph Convolutional Networks via Rewiring

no code implementations10 Jun 2019 Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.

General Classification Graph Classification +1

From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features

no code implementations NIPS 2018 2018 Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Liang Zhao, Yinglong Xia, Michael Witbrock

Graph kernels are one of the most important methods for graph data analysis and have been successfully applied in diverse applications.

Graph Embedding

The Cinderella Complex: Word Embeddings Reveal ender Stereotypes in Movies and Books

1 code implementation12 Nov 2018 Huimin Xu, Zhang Zhang, Lingfei Wu, Cheng-Jun Wang

Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling.

Word Embeddings

Word Mover's Embedding: From Word2Vec to Document Embedding

1 code implementation EMNLP 2018 Lingfei Wu, Ian E. H. Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock

While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings.

Document Embedding General Classification +6

Random Warping Series: A Random Features Method for Time-Series Embedding

1 code implementation14 Sep 2018 Lingfei Wu, Ian En-Hsu Yen, Jin-Feng Yi, Fangli Xu, Qi Lei, Michael Witbrock

The proposed kernel does not suffer from the issue of diagonal dominance while naturally enjoys a \emph{Random Features} (RF) approximation, which reduces the computational complexity of existing DTW-based techniques from quadratic to linear in terms of both the number and the length of time-series.

Clustering Dynamic Time Warping +2

SQL-to-Text Generation with Graph-to-Sequence Model

1 code implementation EMNLP 2018 Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin

Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query.

Graph-to-Sequence SQL-to-Text +1

Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability

2 code implementations14 Sep 2018 Lingfei Wu, Ian E. H. Yen, Jie Chen, Rui Yan

We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

1 code implementation EMNLP 2018 Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Li-Wei Chen, Vadim Sheinin

Existing neural semantic parsers mainly utilize a sequence encoder, i. e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees.

Graph-to-Sequence Semantic Parsing

Quantized Densely Connected U-Nets for Efficient Landmark Localization

1 code implementation ECCV 2018 Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas

Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.

Face Alignment Pose Estimation

Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications

1 code implementation30 May 2018 Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse

The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.

Anomaly Detection Computational Efficiency +1

Deep Graph Translation

2 code implementations25 May 2018 Xiaojie Guo, Lingfei Wu, Liang Zhao

To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs.

Management Translation

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

4 code implementations ICLR 2019 Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, Vadim Sheinin

Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.

Decoder Graph-to-Sequence +2

D2KE: From Distance to Kernel and Embedding

no code implementations14 Feb 2018 Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock

For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more naturally than a feature representation.

Time Series Analysis

Revisiting Spectral Graph Clustering with Generative Community Models

no code implementations14 Sep 2017 Pin-Yu Chen, Lingfei Wu

The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs).

Clustering Community Detection +2

Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It

1 code implementation7 Sep 2017 Lingfei Wu, Dashun Wang, James A. Evans

Teams dominate the production of high-impact science and technology.

Physics and Society Digital Libraries Social and Information Networks

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