Search Results for author: Wen Zhang

Found 45 papers, 14 papers with code

Standing on the Shoulders of Predecessors: Meta-Knowledge Transfer for Knowledge Graphs

no code implementations27 Oct 2021 Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen

We call such knowledge meta-knowledge, and refer to the problem of transferring meta-knowledge from constructed (source) KGs to new (target) KGs to improve the performance of tasks on target KGs as meta-knowledge transfer for knowledge graphs.

Entity Embeddings Knowledge Graph Embedding +3

3D-Transformer: Molecular Representation with Transformer in 3D Space

1 code implementation4 Oct 2021 Fang Wu, Qiang Zhang, Dragomir Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, Huajun Chen

These papers, however, are computationally expensive in capturing long-range dependencies of input atoms; and have not considered the non-uniformity of interatomic distances, thus failing to learn context-dependent representations at different scales.

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 League of Legends

FedE: Embedding Knowledge Graphs in Federated Setting

1 code implementation24 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 Completion Knowledge Graph Embedding +1

DistilE: Distiling Knowledge Graph Embeddings for Faster and Cheaper Reasoning

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

Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and usually a higher dimensional one ensures better reasoning capability.

Knowledge Distillation Knowledge Graph Embedding +2

A Survey on Negative Transfer

no code implementations2 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.

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.

Variational Wasserstein Barycenters for Geometric Clustering

1 code implementation24 Feb 2020 Liang Mi, Tianshu Yu, Jose Bento, Wen Zhang, Baoxin Li, Yalin Wang

We propose to compute Wasserstein barycenters (WBs) by solving for Monge maps with variational principle.

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 +1

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 Translation

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

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.

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.

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 Speech Quality

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 Extraction +1

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 Translation

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 Translation

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 Translation

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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