Search Results for author: Wen Zhang

Found 106 papers, 52 papers with code

Tencent Translation System for the WMT21 News Translation Task

no code implementations WMT (EMNLP) 2021 Longyue Wang, Mu Li, Fangxu Liu, Shuming Shi, Zhaopeng Tu, Xing Wang, Shuangzhi Wu, Jiali Zeng, Wen Zhang

Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.

Data Augmentation Translation

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training

1 code implementation COLING 2022 Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.

Machine Translation NMT +2

MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion

2 code implementations15 Apr 2024 Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Huajun Chen, Wen Zhang

To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities.

Contrastive Learning Descriptive +3

Prompt-fused framework for Inductive Logical Query Answering

no code implementations19 Mar 2024 Zezhong Xu, Peng Ye, Lei Liang, Huajun Chen, Wen Zhang

Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning.

Knowledge Graphs

The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework

1 code implementation11 Mar 2024 Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Huajun Chen, Wen Zhang

In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA).

Knowledge Graph Completion Misconceptions +3

Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion

1 code implementation22 Feb 2024 Yichi Zhang, Zhuo Chen, Lei Liang, Huajun Chen, Wen Zhang

To address the mentioned problems, we propose Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality information for MMKGC.

Multi-modal Knowledge Graph

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

3 code implementations8 Feb 2024 Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng, Lingbing Guo, Xiang Chen, Qian Li, Wen Zhang, Jiaoyan Chen, Yushan Zhu, Jiaqi Li, Xiaoze Liu, Jeff Z. Pan, Ningyu Zhang, Huajun Chen

In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.

Entity Alignment Image Classification +4

Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

2 code implementations9 Jan 2024 Junjie Wang, Dan Yang, Binbin Hu, Yue Shen, Ziqi Liu, Wen Zhang, Jinjie Gu, Zhiqiang Zhang

Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue.

Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation

no code implementations4 Jan 2024 Zhengyi Li, Menglu Li, Lida Zhu, Wen Zhang

Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations. Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction.

Contrastive Learning Representation Learning

A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation

1 code implementation25 Dec 2023 Yongkang Wang, Xuan Liu, Feng Huang, Zhankun Xiong, Wen Zhang

Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases.

Contrastive Learning

Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model

1 code implementation4 Dec 2023 Yuxia Geng, Jiaoyan Chen, Yuhang Zeng, Zhuo Chen, Wen Zhang, Jeff Z. Pan, Yuxiang Wang, Xiaoliang Xu

Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information.

Knowledge Graph Completion Language Modelling

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

1 code implementation11 Nov 2023 Yichi Zhang, Zhuo Chen, Yin Fang, Yanxi Lu, Fangming Li, Wen Zhang, Huajun Chen

Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especially in ensuring that responses are both accommodating to user requirements and appropriately leveraging domain-specific knowledge bases.

Knowledge Graphs Question Answering

Sound field reconstruction using neural processes with dynamic kernels

no code implementations9 Nov 2023 Zining Liang, Wen Zhang, Thushara D. Abhayapala

Accurately representing the sound field with the high spatial resolution is critical for immersive and interactive sound field reproduction technology.

Gaussian Processes

Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding

1 code implementation15 Oct 2023 Xiangnan Chen, Wen Zhang, Zhen Yao, Mingyang Chen, Siliang Tang

Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples.

Denoising Knowledge Graph Completion +2

Making Large Language Models Perform Better in Knowledge Graph Completion

1 code implementation10 Oct 2023 Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Wen Zhang, Huajun Chen

In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning.

In-Context Learning Knowledge Graph Completion +2

A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

1 code implementation15 Aug 2023 Long Jin, Zhen Yao, Mingyang Chen, Huajun Chen, Wen Zhang

Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.

Knowledge Graph Completion Knowledge Graph Embedding +1

MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion

1 code implementation13 Aug 2023 Yichi Zhang, Zhuo Chen, Wen Zhang

Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs.

Multi-modal Knowledge Graph

Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

1 code implementation30 Jul 2023 Zhuo Chen, Lingbing Guo, Yin Fang, Yichi Zhang, Jiaoyan Chen, Jeff Z. Pan, Yangning Li, Huajun Chen, Wen Zhang

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.

 Ranked #1 on Multi-modal Entity Alignment on UMVM-oea-d-w-v2 (using extra training data)

Benchmarking Knowledge Graph Embeddings +2

CausE: Towards Causal Knowledge Graph Embedding

1 code implementation21 Jul 2023 Yichi Zhang, Wen Zhang

Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC).

Disentanglement Knowledge Graph Completion +1

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

1 code implementation8 Jun 2023 Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).

Drug Discovery Graph Learning +1

Exploring Better Text Image Translation with Multimodal Codebook

1 code implementation27 May 2023 Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.

Machine Translation Optical Character Recognition +2

Towards Better Entity Linking with Multi-View Enhanced Distillation

1 code implementation27 May 2023 Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Denvy Deng, Qi Zhang

Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.

Entity Linking Knowledge Distillation +1

Revisit and Outstrip Entity Alignment: A Perspective of Generative Models

no code implementations24 May 2023 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yin Fang, Wen Zhang, Huajun Chen

We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).

Entity Alignment Generative Adversarial Network

Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations

2 code implementations6 May 2023 Yufeng Huang, Jiji Tang, Zhuo Chen, Rongsheng Zhang, Xinfeng Zhang, WeiJie Chen, Zeng Zhao, Zhou Zhao, Tangjie Lv, Zhipeng Hu, Wen Zhang

In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.

Image-text matching Text Matching

NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning

1 code implementation28 Apr 2023 Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.

Graph Representation Learning Knowledge Graphs

Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding

1 code implementation23 Apr 2023 Yichi Zhang, Mingyang Chen, Wen Zhang

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training.

Knowledge Graph Embedding Multi-modal Knowledge Graph

Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer

1 code implementation3 Mar 2023 Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen

Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.

Image Classification Knowledge Graphs +3

Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation

1 code implementation2 Mar 2023 Mengge Liu, Wen Zhang, Xiang Li, Jian Luan, Bin Wang, Yuhang Guo, Shuoying Chen

Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence.

Machine Translation Sentence +1

Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

1 code implementation3 Feb 2023 Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, Huajun Chen

In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.

Entity Embeddings Knowledge Graph Embedding +3

Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs

no code implementations3 Feb 2023 Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen

In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation.

Knowledge Graph Embedding Knowledge Graphs

Analogical Inference Enhanced Knowledge Graph Embedding

1 code implementation3 Jan 2023 Zhen Yao, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, Huajun Chen

And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.

Knowledge Graph Embedding Knowledge Graphs +1

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

1 code implementation29 Dec 2022 Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.

 Ranked #1 on Entity Alignment on FBYG15k (using extra training data)

Knowledge Graphs Multi-modal Entity Alignment

Tele-Knowledge Pre-training for Fault Analysis

1 code implementation20 Oct 2022 Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, Xinliang Wu, Yi Yang, Mingyi Chen, Zhaoyang Lian, YingYing Li, Lei Cheng, Huajun Chen

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.

Language Modelling

Neural-Symbolic Entangled Framework for Complex Query Answering

no code implementations19 Sep 2022 Zezhong Xu, Wen Zhang, Peng Ye, Hui Chen, Huajun Chen

In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.

Complex Query Answering Link Prediction +1

Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling

no code implementations15 Sep 2022 Yichi Zhang, Wen Zhang

Twins negative sampling is suitable for multimodal scenarios and could align different embeddings for entities.

Link Prediction World Knowledge

Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

1 code implementation19 Aug 2022 Yufeng Huang, Zhuo Chen, Jiaoyan Chen, Jeff Z. Pan, Zhen Yao, Wen Zhang

Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.

Image Captioning Sentence +2

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

2 code implementations4 Jul 2022 Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Wen Zhang, Yin Fang, Jeff Z. Pan, Huajun Chen

Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.

Attribute Contrastive Learning +4

Disentangled Ontology Embedding for Zero-shot Learning

1 code implementation8 Jun 2022 Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen

In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.

Image Classification Ontology Embedding +2

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

1 code implementation10 May 2022 Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, Huajun Chen

We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.

Knowledge Graphs Link Prediction +1

Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

1 code implementation22 Mar 2022 Zhaoyang Chu, Shichao Liu, Wen Zhang

The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction.

Drug Discovery Graph Learning +1

PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

no code implementations2 Mar 2022 Wen Zhang, Chi-Man Wong, Ganqinag Ye, Bo Wen, Hongting Zhou, Wei zhang, Huajun Chen

On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.

Knowledge Graphs Sequential Recommendation

Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective

no code implementations15 Feb 2022 Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.

Joint-optimization of Node placement and UAV's Trajectory for Self-sustaining Air-Ground IoT system

no code implementations7 Feb 2022 Wen Zhang, Wenlu Wang, Mehdi Sookhak, Chen Pan

Due to the sustainable power supply and environment-friendly features, self-powered IoT devices have been increasingly employed in various fields such as providing observation data in remote areas, especially in rural areas or post-disaster scenarios.

How Good Is Aesthetic Ability of a Fashion Model?

no code implementations CVPR 2022 Xingxing Zou, Kaicheng Pang, Wen Zhang, Waikeung Wong

To date, it is the first work to address the AI model's aesthetic ability with detailed characterization based on the professional fashion domain knowledge.

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

no code implementations18 Dec 2021 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, Huajun Chen

Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.

Data Augmentation Few-Shot Learning +10

HampDTI: a heterogeneous graph automatic meta-path learning method for drug-target interaction prediction

no code implementations16 Dec 2021 Hongzhun Wang, Feng Huang, Wen Zhang

More importantly, HampDTI identifies the important meta-paths for DTI prediction, which could explain how drugs connect with targets in HNs.

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

no code implementations16 Dec 2021 Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen

We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.

Entity Embeddings Graph Attention +4

Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training

no code implementations8 Dec 2021 Ganqiang Ye, Wen Zhang, Zhen Bi, Chi Man Wong, Chen Hui, Huajun Chen

Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.

Entity Alignment Graph Representation Learning +3

Molecular Contrastive Learning with Chemical Element Knowledge Graph

1 code implementation1 Dec 2021 Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, Huajun Chen

To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.

Contrastive Learning Molecular Property Prediction +3

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

1 code implementation27 Oct 2021 Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen

In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.

Entity Embeddings Inductive Relation Prediction +6

Explaining Knowledge Graph Embedding via Latent Rule Learning

no code implementations29 Sep 2021 Wen Zhang, Mingyang Chen, Zezhong Xu, Yushan Zhu, Huajun Chen

KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.

Knowledge Distillation Knowledge Graph Embedding +3

Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

no code implementations2 May 2021 Wen Zhang, Chi-Man Wong, Ganqiang Ye, Bo Wen, Wei zhang, Huajun Chen

As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.

Knowledge Graphs

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

no code implementations30 Apr 2021 Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen

We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

Click-Through Rate Prediction Knowledge Graph Embedding +1

EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learning

no code implementations31 Dec 2020 Shuai Liu, Xinran Xu, Zhihao Yang, Xiaohan Zhao, Wen Zhang

The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveal that the communicative learning module can bring explicit information about EPIs, which is ignore by CNN.

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations25 Nov 2020 Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

Towards Playing Full MOBA Games with Deep Reinforcement Learning

no code implementations NeurIPS 2020 Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.

Dota 2 reinforcement-learning +1

FedE: Embedding Knowledge Graphs in Federated Setting

2 code implementations24 Oct 2020 Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, Huajun Chen

Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.

Knowledge Graph Embedding Knowledge Graph Embeddings

Distributed Representations of Entities in Open-World Knowledge Graphs

no code implementations16 Oct 2020 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpo Bo, Yin Fang, Xiaoze Liu, Huajun Chen, Wen Zhang

DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings.

Entity Alignment Graph Representation Learning +2

DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning

no code implementations13 Sep 2020 Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei zhang, Huajun Chen

In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.

Knowledge Distillation Knowledge Graph Embedding +2

A Survey on Negative Transfer

1 code implementation2 Sep 2020 Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu

Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain.

Multi-Task Learning

Deep Representation Learning For Multimodal Brain Networks

no code implementations19 Jul 2020 Wen Zhang, Liang Zhan, Paul Thompson, Yalin Wang

The higher-order network mappings from brain structural networks to functional networks are learned in the node domain.

Anatomy Graph Representation Learning

Neural Entity Summarization with Joint Encoding and Weak Supervision

1 code implementation1 May 2020 Junyou Li, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, Huajun Chen

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.

Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation

1 code implementation1 Dec 2019 Wen Zhang, Dongrui Wu

Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the conditional distribution discrepancy; however, a more natural metric may be their joint probability distribution discrepancy.

Domain Adaptation General Classification +2

Modeling Fluency and Faithfulness for Diverse Neural Machine Translation

1 code implementation30 Nov 2019 Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, Dong Yu

Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution.

Machine Translation Translation

Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations

1 code implementation13 Nov 2019 Feng Huang, Xiang Yue, Zhankun Xiong, Zhouxin Yu, Wen Zhang

To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task.

Knowledge Graphs Link Prediction +1

Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation

no code implementations5 Nov 2019 Yong Shan, Yang Feng, Jinchao Zhang, Fandong Meng, Wen Zhang

Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.

Machine Translation Translation

ItLnc-BXE: a Bagging-XGBoost-ensemble method with multiple features for identification of plant lncRNAs

1 code implementation1 Nov 2019 Guangyan Zhang, Ziru Liu, Jichen Dai, Zilan Yu, Shuai Liu, Wen Zhang

However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification.

Ensemble Learning feature selection

Redistribution Mechanism on Networks

no code implementations21 Oct 2019 Wen Zhang, Dengji Zhao, Han-Yu Chen

Redistribution mechanisms have been proposed for more efficient resource allocation but not for profit.

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

1 code implementation14 Oct 2019 Wen Zhang, Dongrui Wu

Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.

Domain Adaptation EEG +2

Geometric Brain Surface Network For Brain Cortical Parcellation

no code implementations13 Sep 2019 Wen Zhang, Yalin Wang

Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results.

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

1 code implementation IJCNLP 2019 Mingyang Chen, Wen Zhang, Wei zhang, Qiang Chen, Huajun Chen

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.

Knowledge Graphs Link Prediction +2

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

4 code implementations12 Jun 2019 Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M. Lin, Wen Zhang, Ping Zhang, Huan Sun

Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.

Graph Embedding Link Prediction +2

Bridging the Gap between Training and Inference for Neural Machine Translation

no code implementations ACL 2019 Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

Machine Translation NMT +2

Neural Learning of Online Consumer Credit Risk

no code implementations5 Jun 2019 Di Wang, Qi Wu, Wen Zhang

This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase.

Time Series Time Series Analysis

Fixed-price Diffusion Mechanism Design

no code implementations14 May 2019 Tianyi Zhang, Dengji Zhao, Wen Zhang, Xuming He

We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially.

Collaborative Data Acquisition

no code implementations14 May 2019 Wen Zhang, Yao Zhang, Dengji Zhao

We consider a requester who acquires a set of data (e. g. images) that is not owned by one party.

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

no code implementations21 Mar 2019 Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen

We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.

Entity Embeddings Knowledge Graphs +1

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

no code implementations12 Mar 2019 Wen Zhang, Bibek Paudel, Wei zhang, Abraham Bernstein, Huajun Chen

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.

Knowledge Graph Embedding Knowledge Graphs +1

Graph Neural Networks for User Identity Linkage

no code implementations6 Mar 2019 Wen Zhang, Kai Shu, Huan Liu, Yalin Wang

In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.

End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking

no code implementations2 Jan 2019 Xingjian Du, Mengyao Zhu, Xuan Shi, Xinpeng Zhang, Wen Zhang, Jingdong Chen

The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality.

Speech Enhancement

Regularized Wasserstein Means for Aligning Distributional Data

1 code implementation2 Dec 2018 Liang Mi, Wen Zhang, Yalin Wang

We propose to align distributional data from the perspective of Wasserstein means.

Domain Adaptation

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

no code implementations EMNLP 2018 Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei zhang, Hai Zhu, Huajun Chen

This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.

Knowledge Graph Embedding Relation +3

Refining Source Representations with Relation Networks for Neural Machine Translation

no code implementations COLING 2018 Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step.

Machine Translation Memorization +2

Refining Source Representations with Relation Networks for Neural Machine Translation

no code implementations12 Sep 2017 Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget old information which is often useful and the encoder only operates through words without considering word relationship.

Machine Translation NMT +2

Information-Propogation-Enhanced Neural Machine Translation by Relation Model

no code implementations6 Sep 2017 Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

Even though sequence-to-sequence neural machine translation (NMT) model have achieved state-of-art performance in the recent fewer years, but it is widely concerned that the recurrent neural network (RNN) units are very hard to capture the long-distance state information, which means RNN can hardly find the feature with long term dependency as the sequence becomes longer.

Machine Translation NMT +4

Towards Evidence-Based Ontology for Supporting Systematic Literature Review

no code implementations22 Sep 2016 Yueming Sun, Ye Yang, He Zhang, Wen Zhang, Qing Wang

[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.

Intrinsic Light Field Images

no code implementations15 Aug 2016 Elena Garces, Jose I. Echevarria, Wen Zhang, Hongzhi Wu, Kun Zhou, Diego Gutierrez

We present a method to automatically decompose a light field into its intrinsic shading and albedo components.

Shape Analysis With Hyperbolic Wasserstein Distance

no code implementations CVPR 2016 Jie Shi, Wen Zhang, Yalin Wang

Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.

Classification General Classification

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