2 code implementations • 28 Sep 2023 • Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, Tianhang Zhu
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on Multi-Label Text Classification on CC3M-TagMask
1 code implementation • 8 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.
1 code implementation • 29 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.
Ranked #2 on Speech Recognition on AISHELL-1
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 26 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.
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.
no code implementations • 17 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.
1 code implementation • 25 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.
no code implementations • 26 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.
no code implementations • 7 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.
1 code implementation • 22 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.
1 code implementation • 28 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.
no code implementations • 17 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.
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.
5 code implementations • 12 Oct 2019 • Zekun Li, Zeyu Cui, Shu Wu, Xiao-Yu Zhang, Liang Wang
The key of this task is to model feature interactions among different feature fields.
Ranked #9 on Click-Through Rate Prediction on Avazu
1 code implementation • 31 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.
1 code implementation • 21 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)