Search Results for author: Zeyu Cui

Found 16 papers, 11 papers with code

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

1 code implementation8 Dec 2022 Jinze Bai, Rui Men, Hao Yang, Xuancheng Ren, Kai Dang, Yichang Zhang, Xiaohuan Zhou, Peng Wang, Sinan Tan, An Yang, Zeyu Cui, Yu Han, Shuai Bai, Wenbin Ge, Jianxin Ma, Junyang Lin, Jingren Zhou, Chang Zhou

As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.

Multi-Task Learning

MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition

1 code implementation29 Nov 2022 Xiaohuan Zhou, JiaMing Wang, Zeyu Cui, Shiliang Zhang, Zhijie Yan, Jingren Zhou, Chang Zhou

Therefore, we propose to introduce the phoneme modality into pre-training, which can help capture modality-invariant information between Mandarin speech and text.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Contextual Expressive Text-to-Speech

no code implementations26 Nov 2022 Jianhong Tu, Zeyu Cui, Xiaohuan Zhou, Siqi Zheng, Kai Hu, Ju Fan, Chang Zhou

To achieve this task, we construct a synthetic dataset and develop an effective framework.

Speech Synthesis

Second-Order Global Attention Networks for Graph Classification and Regression

1 code implementation Conference 2022 Fenyu Hu, Zeyu Cui, Shu Wu, Qiang Liu, Jinlin Wu, Liang Wang & Tieniu Tan

Graph Neural Networks (GNNs) are powerful to learn representation of graph-structured data, which fuse both attributive and topological information.

Graph Classification Graph Regression +1

M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

no code implementations17 May 2022 Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang

Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production.

Computational Efficiency Explanation Generation +3

GraphFM: Graph Factorization Machines for Feature Interaction Modeling

1 code implementation25 May 2021 Shu Wu, Zekun Li, Yunyue Su, Zeyu Cui, XiaoYu Zhang, Liang Wang

To solve the problems, we propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure.

Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation

no code implementations26 Apr 2021 Yinjiang Cai, Zeyu Cui, Shu Wu, Zhen Lei, Xibo Ma

Our proposed Co-occurrence based Enhanced Representation model (CER) learns the scoring function by a deep neural network with the attentive user representation and fusion of raw representation and enhanced representation of target item as input.

Collaborative Filtering Recommendation Systems

DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

no code implementations7 Apr 2021 Zeyu Cui, Zekun Li, Shu Wu, XiaoYu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai

We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings.

Dynamic graph embedding

Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval

1 code implementation22 Feb 2021 Xueli Yu, Weizhi Xu, Zeyu Cui, Shu Wu, Liang Wang

In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level.


A Graph-based Relevance Matching Model for Ad-hoc Retrieval

1 code implementation28 Jan 2021 Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang

To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial.


Disentangled Item Representation for Recommender Systems

no code implementations17 Aug 2020 Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang

In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation.

Attribute Recommendation Systems

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

1 code implementation ACL 2020 Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang

We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.

Document Embedding General Classification +2

Semi-supervised Compatibility Learning Across Categories for Clothing Matching

1 code implementation31 Jul 2019 Zekun Li, Zeyu Cui, Shu Wu, Xiao-Yu Zhang, Liang Wang

To achieve the alignment, we minimize the distances between distributions with unsupervised adversarial learning, and also the distances between some annotated compatible items which play the role of anchor points to help align.

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

1 code implementation21 Feb 2019 Zeyu Cui, Zekun Li, Shu Wu, Xiao-Yu Zhang, Liang Wang

In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit".

 Ranked #1 on Recommendation Systems on Polyvore (Accuracy metric)

Recommendation Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.