Search Results for author: Fuzhen Zhuang

Found 72 papers, 35 papers with code

Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation

no code implementations29 Aug 2023 Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong

Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones.

Knowledge Graphs

Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification

1 code implementation10 Aug 2023 Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang

Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.


Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction

1 code implementation4 Aug 2023 Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang

Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives.

Classification Graph Learning +1

Scaling Sentence Embeddings with Large Language Models

1 code implementation31 Jul 2023 Ting Jiang, Shaohan Huang, Zhongzhi Luan, Deqing Wang, Fuzhen Zhuang

We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.

Contrastive Learning Semantic Textual Similarity +2

Modeling Dual Period-Varying Preferences for Takeaway Recommendation

no code implementations7 Jun 2023 Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu

Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling.

Recommendation Systems

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

no code implementations27 May 2023 Hao Geng, Deqing Wang, Fuzhen Zhuang, Xuehua Ming, Chenguang Du, Ting Jiang, Haolong Guo, Rui Liu

To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.

Citation Prediction Network Embedding

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

1 code implementation18 May 2023 Chenguang Du, Kaichun Yao, HengShu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong

However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.

Information Retrieval Representation Learning +1

Attacking Pre-trained Recommendation

no code implementations6 May 2023 Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie zhou, Yongjun Xu, Qing He

Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks.

Sequential Recommendation

Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation

1 code implementation28 Apr 2023 Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Victor S. Sheng

Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.

Contrastive Learning Knowledge Distillation +2

Frequency Enhanced Hybrid Attention Network for Sequential Recommendation

1 code implementation18 Apr 2023 Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Victor S. Sheng

However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.

Contrastive Learning Sequential Recommendation

Meta-optimized Contrastive Learning for Sequential Recommendation

1 code implementation16 Apr 2023 Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng

By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.

Contrastive Learning Data Augmentation +2

A Survey on Causal Inference for Recommendation

no code implementations21 Mar 2023 Huishi Luo, Fuzhen Zhuang, Ruobing Xie, HengShu Zhu, Deqing Wang

Recently, causal inference has attracted increasing attention from researchers of recommender systems (RS), which analyzes the relationship between a cause and its effect and has a wide range of real-world applications in multiple fields.

Causal Inference Data Augmentation +1

Graph Learning and Its Applications: A Holistic Survey

no code implementations17 Dec 2022 Shaopeng Wei, Yu Zhao, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.

Graph Learning Representation Learning

A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective

no code implementations28 Nov 2022 Yu Zhao, Huaming Du, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).


RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

1 code implementation20 Nov 2022 Ziming Wan, Deqing Wang, Xuehua Ming, Fuzhen Zhuang, Chenguang Du, Ting Jiang, Zhengyang Zhao

To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning.

Contrastive Learning Graph Representation Learning

Pruning Pre-trained Language Models Without Fine-Tuning

1 code implementation12 Oct 2022 Ting Jiang, Deqing Wang, Fuzhen Zhuang, Ruobing Xie, Feng Xia

These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.

A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

1 code implementation COLING 2022 Wei Chen, Jinglong Du, Zhao Zhang, Fuzhen Zhuang, Zhongshi He

Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification).

Aspect Extraction Sentiment Analysis +1

Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition

2 code implementations23 Jul 2022 Chuanguang Yang, Zhulin An, Helong Zhou, Fuzhen Zhuang, Yongjun Xu, Qian Zhan

This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks.

Contrastive Learning Image Classification +3

Customized Conversational Recommender Systems

no code implementations30 Jun 2022 Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He, Hui Xiong

In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.

Meta-Learning Recommendation Systems

Memory-Guided Multi-View Multi-Domain Fake News Detection

1 code implementation26 Jun 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, Fuzhen Zhuang

In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.

Fake News Detection

TAR: Neural Logical Reasoning across TBox and ABox

no code implementations29 May 2022 Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf

Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.

Descriptive Logical Reasoning

Personalized Prompt for Sequential Recommendation

no code implementations19 May 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Qing He

Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations.

Contrastive Learning Sequential Recommendation

Detect Professional Malicious User with Metric Learning in Recommender Systems

no code implementations19 May 2022 Yuanbo Xu, Yongjian Yang, En Wang, Fuzhen Zhuang, Hui Xiong

2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.

Metric Learning Outlier Detection +1

Selective Fairness in Recommendation via Prompts

1 code implementation10 May 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He

In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free.

Fairness Sequential Recommendation

Exploiting Global and Local Hierarchies for Hierarchical Text Classification

1 code implementation5 May 2022 Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang

Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.

Multi Label Text Classification Multi-Label Text Classification +1

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection

1 code implementation20 Apr 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang

In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.

Fake News Detection

User-Centric Conversational Recommendation with Multi-Aspect User Modeling

1 code implementation20 Apr 2022 Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He

In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.

Dialogue Generation Dialogue Understanding +1

Multi-view Multi-behavior Contrastive Learning in Recommendation

1 code implementation20 Mar 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He

We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.

Contrastive Learning

Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

1 code implementation1 Feb 2022 Yu Zhao, Shaopeng Wei, Yu Guo, Qing Yang, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

1 code implementation11 Jan 2022 Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.

Implicit Relations Stock Prediction

Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection

no code implementations4 Jan 2022 Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He

Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).

Fraud Detection

Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources

1 code implementation4 Jan 2022 Yongchun Zhu, Fuzhen Zhuang, Deqing Wang

However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other.

Image Classification Multi-Source Unsupervised Domain Adaptation +1

Multi-Representation Adaptation Network for Cross-domain Image Classification

1 code implementation4 Jan 2022 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He

Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects.

Classification Domain Adaptation +2

Neural Hierarchical Factorization Machines for User's Event Sequence Analysis

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He

Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.

Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He

Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.

Domain Adaptation with Category Attention Network for Deep Sentiment Analysis

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He

Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions.

Classification Domain Adaptation +2

Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, HengShu Zhu, Pengpeng Zhao, Chang Tan, Qing He

To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.

Recommendation Systems

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

no code implementations27 Dec 2021 Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He

The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.

Cross-Lingual Information Retrieval Retrieval

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks

1 code implementation24 Dec 2021 Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.

Graph Learning

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

1 code implementation NeurIPS 2021 Ying Sun, HengShu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong

To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).

Decision Making

Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment

no code implementations1 Dec 2021 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Hao liu, Fuzhen Zhuang, Hui Xiong, Haifeng Chen

Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.

Entity Alignment

Deep Keyphrase Completion

no code implementations29 Oct 2021 Yu Zhao, Jia Song, Huali Feng, Fuzhen Zhuang, Qing Li, Xiaojie Wang, Ji Liu

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.

Keyphrase Extraction Keyphrase Generation +2

Personalized Transfer of User Preferences for Cross-domain Recommendation

1 code implementation21 Oct 2021 Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He

Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.

Recommendation Systems

Improving Non-autoregressive Generation with Mixup Training

1 code implementation21 Oct 2021 Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, Qi Zhang

While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.

Natural Language Understanding Paraphrase Generation +2

Deep Subdomain Adaptation Network for Image Classification

1 code implementation17 Jun 2021 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He

The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.

Classification Domain Adaptation +4

AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction

no code implementations16 Jun 2021 Hao Chen, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, Qing He

The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information.

Disease Prediction text similarity

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

3 code implementations31 May 2021 Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, Juan Cao

Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.

Marketing Meta-Learning +1

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

3 code implementations18 May 2021 Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, Yu Chen

While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.

Multi-Task Learning

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

no code implementations11 May 2021 Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, Qing He

With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.

Meta-Learning Recommendation Systems

Combat Data Shift in Few-shot Learning with Knowledge Graph

no code implementations27 Jan 2021 Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, Juan Cao, Qing He

However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.

Few-Shot Learning

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

no code implementations18 Jan 2021 Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang

Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

1 code implementation9 Jan 2021 Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, Fuzhen Zhuang

In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.

General Classification Multi Label Text Classification +2

E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

no code implementations7 Sep 2020 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Fuzhen Zhuang, Haifeng Chen, Hui Xiong

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.

Aspect Extraction Denoising +4

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

no code implementations8 Aug 2020 Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He

In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.

Fraud Detection Management

Graph Factorization Machines for Cross-Domain Recommendation

no code implementations12 Jul 2020 Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He

In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.

Recommendation Systems

Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

1 code implementation13 Apr 2020 Jingjing Gu, Qiang Zhou, Jingyuan Yang, Yanchi Liu, Fuzhen Zhuang, Yanchao Zhao, Hui Xiong

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.

Clustering Management

A Survey on Knowledge Graph-Based Recommender Systems

no code implementations28 Feb 2020 Qingyu Guo, Fuzhen Zhuang, Chuan Qin, HengShu Zhu, Xing Xie, Hui Xiong, Qing He

On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.

Explainable Recommendation Recommendation Systems

Transfer Learning Toolkit: Primers and Benchmarks

2 code implementations20 Nov 2019 Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu, Dongbo Xi, Zhiyuan Qi, Qing He

The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.

Transfer Learning

A Comprehensive Survey on Transfer Learning

3 code implementations7 Nov 2019 Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He

In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.

Transfer Learning

Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning

no code implementations11 Oct 2019 Changying Du, Jia He, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.


Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling

no code implementations10 Oct 2019 Changying Du, Fuzhen Zhuang, Jia He, Qing He, Guoping Long

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.

Knowledge Graph Embedding with Hierarchical Relation Structure

no code implementations EMNLP 2018 Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, Qing He

To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.

Information Retrieval Knowledge Base Completion +3

Cross-Domain Labeled LDA for Cross-Domain Text Classification

1 code implementation16 Sep 2018 Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu

To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA).

Cross-Domain Text Classification General Classification +1

The Automatic Identification of Butterfly Species

no code implementations18 Mar 2018 Juanying Xie, Qi Hou, Yinghuan Shi, Lv Peng, Liping Jing, Fuzhen Zhuang, Junping Zhang, Xiaoyang Tang, Shengquan Xu

We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.

Policy Gradients for Contextual Recommendations

no code implementations12 Feb 2018 Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He

We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.

Decision Making Multi-Armed Bandits +2

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