Search Results for author: Lihong Wang

Found 22 papers, 14 papers with code

Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering

no code implementations24 May 2024 Yibo Zhang, Lihong Wang, Changqing Zou, Tieru Wu, Rui Ma

Specifically, we perform perspective projection to render the 3D rational B\'ezier curves into 2D curves, which are subsequently converted to a 2D raster image via our customized differentiable rasterizer.

Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

1 code implementation13 Mar 2024 Lintao Zhang, Mengqi Wu, Lihong Wang, David C. Steffens, Guy G. Potter, Mingxia Liu

To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model.

Anatomy Image Denoising

Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment

1 code implementation10 Oct 2023 Qian Li, Cheng Ji, Shu Guo, Zhaoji Liang, Lihong Wang, JianXin Li

To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.

Knowledge Graphs Multi-modal Entity Alignment +1

Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI

no code implementations20 Jun 2023 Lintao Zhang, Jinjian Wu, Lihong Wang, Li Wang, David C. Steffens, Shijun Qiu, Guy G. Potter, Mingxia Liu

Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i. e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification.

Anatomy MRI Reconstruction +1

Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment

no code implementations4 Apr 2023 Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, JianXin Li

In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.

Attribute Graph Representation Learning +3

Reinforcement Learning Guided Multi-Objective Exam Paper Generation

1 code implementation2 Mar 2023 Yuhu Shang, Xuexiong Luo, Lihong Wang, Hao Peng, Xiankun Zhang, Yimeng Ren, Kun Liang

To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria.

Knowledge Tracing Paper generation +2

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

1 code implementation24 Dec 2022 Lintao Zhang, Lihong Wang, Minhui Yu, Rong Wu, David C. Steffens, Guy G. Potter, Mingxia Liu

In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data.

cognitive diagnosis Representation Learning

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

no code implementations15 Nov 2022 Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie

To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.

Event Detection Semantic Similarity +2

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Event Extraction Graph Attention +2

Transferring Knowledge Distillation for Multilingual Social Event Detection

1 code implementation6 Aug 2021 Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang

Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.

Cross-Lingual Word Embeddings Event Detection +2

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +3

Attend and select: A segment selective transformer for microblog hashtag generation

1 code implementation6 Jun 2021 Qianren Mao, Xi Li, Bang Liu, Shu Guo, Peng Hao, JianXin Li, Lihong Wang

These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.

Knowledge Graph Completion Link Prediction

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

General Classification Multi Label Text Classification +3

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren

Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.

Link Prediction Multi-Label Classification +1

Modeling relation paths for knowledge base completion via joint adversarial training

1 code implementation14 Oct 2018 Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, Philip S. Yu, Min He, Linfeng Du, Lihong Wang

By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.

Knowledge Base Completion Relation

Knowledge Graph Embedding with Iterative Guidance from Soft Rules

1 code implementation30 Nov 2017 Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo

In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules.

Knowledge Graph Embedding Knowledge Graphs +1

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