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51. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Author

Guo, Huifeng, Tang, Ruiming, Ye, Yunming, Li, Zhenguo, and He, Xiuqiang

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide \& Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

Published
2017

52. A Graph-based Push Service Platform

Author

Guo, Huifeng, Tang, Ruiming, Ye, Yunming, Li, Zhenguo, and He, Xiuqiang

Subjects
Computer Science - Information Retrieval
Abstract

It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line system. There are two different modes of recommendation systems, namely pull-mode and push-mode. The majority of the recommendation systems are pull-mode, which recommend items to users only when and after users enter Application Market. While push-mode works more actively to enhance or re-build connection between Application Market and users. As one of the most successful phone manufactures,both the number of users and apps increase dramatically in Huawei Application Store (also named Hispace Store), which has approximately 0.3 billion registered users and 1.2 million apps until 2016 and whose number of users is growing with high-speed. For the needs of real scenario, we establish a Push Service Platform (shortly, PSP) to discover the target user group automatically from web-scale user operation log data with an additional small set of labelled apps (usually around 10 apps),in Hispace Store. As presented in this work,PSP includes distributed storage layer, application layer and evaluation layer. In the application layer, we design a practical graph-based algorithm (named A-PARW) for user group discovery, which is an approximate version of partially absorbing random walk. Based on I mode of A-PARW, the effectiveness of our system is significantly improved, compared to the predecessor to presented system, which uses Personalized Pagerank in its application layer.

Published
2016

58. A Multivariate Time Series Classification Method Based on Self-attention

Author

Lin, Huiwei, Ye, Yunming, Leung, Ka-Cheong, Zhang, Bowen, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pan, Jeng-Shyang, editor, Lin, Jerry Chun-Wei, editor, Liang, Yongquan, editor, and Chu, Shu-Chuan, editor

Published
2020
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59. A Noise Adaptive Model for Distantly Supervised Relation Extraction

Author

Huang, Xu, Zhang, Bowen, Ye, Yunming, Chen, Xiaojun, Li, Xutao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhu, Xiaodan, editor, Zhang, Min, editor, Hong, Yu, editor, and He, Ruifang, editor

Published
2020
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60. Aligning Users Across Social Networks via Intra and Inter Attentions

Author

Huang, Zhichao, Li, Xutao, Ye, Yunming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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65. TinyPredNet: A Lightweight Framework for Satellite Image Sequence Prediction.

Author

Dai, Kuai, Li, Xutao, Lin, Huiwei, Jiang, Yin, Chen, Xunlai, Ye, Yunming, and Xian, Di

Subjects
REMOTE-sensing images, LANDSAT satellites, DEEP learning, FORECASTING, MOTION capture (Human mechanics), NATURAL satellites
Abstract

Satellite image sequence prediction aims to precisely infer future satellite image frames with historical observations, which is a significant and challenging dense prediction task. Though existing deep learning models deliver promising performance for satellite image sequence prediction, the methods suffer from quite expensive training costs, especially in training time and GPU memory demand, due to the inefficiently modeling for temporal variations. This issue seriously limits the lightweight application in satellites such as space-borne forecast models. In this article, we propose a lightweight prediction framework TinyPredNet for satellite image sequence prediction, in which a spatial encoder and decoder model the intra-frame appearance features and a temporal translator captures inter-frame motion patterns. To efficiently model the temporal evolution of satellite image sequences, we carefully design a multi-scale temporal-cascaded structure and a channel attention-gated structure in the temporal translator. Comprehensive experiments are conducted on FengYun-4A (FY-4A) satellite dataset, which show that the proposed framework achieves very competitive performance with much lower computation cost compared to state-of-the-art methods. In addition, corresponding interpretability experiments are conducted to show how our designed structures work. We believe the proposed method can serve as a solid lightweight baseline for satellite image sequence prediction. [ABSTRACT FROM AUTHOR]

Published
2024
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66. A Hierarchical Model with Recurrent Convolutional Neural Networks for Sequential Sentence Classification

Author

Jiang, Xinyu, Zhang, Bowen, Ye, Yunming, Liu, Zhenhua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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67. Learning Stance Classification with Recurrent Neural Capsule Network

Author

Sun, Lianjie, Li, Xutao, Zhang, Bowen, Ye, Yunming, Xu, Baoxun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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68. Learning Personalized End-to-End Task-Oriented Dialogue Generation

Author

Zhang, Bowen, Xu, Xiaofei, Li, Xutao, Ye, Yunming, Chen, Xiaojun, Sun, Lianjie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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69. A Novel KNN Approach for Session-Based Recommendation

Author

Guo, Huifeng, Tang, Ruiming, Ye, Yunming, Liu, Feng, Zhang, Yuzhou, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Yang, Qiang, editor, Zhou, Zhi-Hua, editor, Gong, Zhiguo, editor, Zhang, Min-Ling, editor, and Huang, Sheng-Jun, editor

Published
2019
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76. Detection of Oil Spill Through Fully Convolutional Network

Author

Li, Yan, Yang, Xiaofei, Ye, Yunming, Cui, Lunan, Jia, Binfeng, Jiang, Zhongming, Wang, Shaokai, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Yuan, Hanning, editor, Geng, Jing, editor, Liu, Chuanlu, editor, Bian, Fuling, editor, and Surapunt, Tisinee, editor

Published
2018
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80. Multi-roles Graph Based Extractive Summarization

Author

Chen, Zhibin, Ye, Yunming, Xu, Xiaofei, Li, Feng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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81. Joint Weighted Nonnegative Matrix Factorization for Mining Attributed Graphs

Author

Huang, Zhichao, Ye, Yunming, Li, Xutao, Liu, Feng, Chen, Huajie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kim, Jinho, editor, Shim, Kyuseok, editor, Cao, Longbing, editor, Lee, Jae-Gil, editor, Lin, Xuemin, editor, and Moon, Yang-Sae, editor

Published
2017
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82. A Graph-Based Push Service Platform

Author

Guo, Huifeng, Tang, Ruiming, Ye, Yunming, Li, Zhenguo, He, Xiuqiang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Candan, Selçuk, editor, Chen, Lei, editor, Pedersen, Torben Bach, editor, Chang, Lijun, editor, and Hua, Wen, editor

Published
2017
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87. Spherical Neural Operator Network for Global Weather Prediction

Author

Lin, Kenghong, Li, Xutao, Ye, Yunming, Feng, Shanshan, Zhang, Baoquan, Xu, Guangning, and Wang, Ziye

Abstract

Global weather forecast is an important spatial-temporal prediction problem, which can provide numerous societal benefits such as extreme weather forewarning, traffic scheduling, and agricultural planning. Though many spatial-temporal prediction models have been proposed, they suffer from two drawbacks for global weather forecasts, namely 1) ignoring the physical mechanism and spherical characteristics and 2) not effectively exploiting the global and local correlations. To address the above drawbacks, in this paper, we formalize global weather state dynamics as partial differential equations (PDEs) in spherical space and infer the state of the global weather system by solving these PDEs. Specifically, we use Green’s function method to solve the PDEs and find that the solution of the spherical PDEs can be obtained by the spherical convolution. We further proposed a novel Spherical Neural Operator, SNO, which consists of spherical convolution and vanilla convolution. The former is used to solve these PDEs and model the global correlations in spherical space, and the latter is used to capture the local correlations. Upon the operator, a global weather prediction model is developed. Extensive experimental results demonstrate the effectiveness and superiority of our method over state-of-the-art approaches.

Published
2024
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88. Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave

Author

Wang, Ziye, Li, Xutao, Lin, Kenghong, Luo, Chuyao, Ye, Yunming, and Hu, Xiuqing

Abstract

Passive microwave (PMW) radiometers have been widely utilized for quantitative precipitation estimation (QPE) by leveraging the relationship between brightness temperature (Tb) and rain rate. Nevertheless, accurate precipitation estimation remains a challenge due to the intricate relationship between them, which is influenced by a diverse range of complex atmospheric and surface properties. In addition, the inherent skew distribution of rainfall values prevents models from correctly addressing extreme precipitation events, leading to a significant underestimation. This article presents a novel model called the multiscale and multilevel feature fusion network (MSMLNet), consisting of two essential components: a multiscale feature extractor and a multilevel regression predictor. The feature extractor is specifically designed to extract characteristics from multiple scales, enabling the model to incorporate various meteorological conditions, as well as atmospheric and surface information in the surrounding environment. The regression predictor first assesses the probabilities of multiple rainfall levels for each observed pixel and then extracts features of different levels separately. The multilevel features are fused according to the predicted probabilities. This approach allows each submodule only to focus on a specific range of precipitation, avoiding the undesirable effects of skew distributions. To evaluate the performance of MSMLNet, various deep learning methods are adapted for the precipitation retrieval task, and a PWM-based product from the global precipitation measurement (GPM) mission is also used for comparison. Extensive experiments show that MSMLNet surpasses GMI-based products and the most advanced deep learning approaches by 17.9% and 2.5% in root mean square error (RMSE), and 54.2% and 4.0% in CSI-10, respectively. Moreover, we demonstrate that MSMLNet significantly mitigates the propensity for underestimating heavy precipitation events and has a consistent and outstanding performance in estimating precipitation across various levels.

Published
2024
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89. Exploring and Exploiting High-Order Spatial–Temporal Dynamics for Long-Term Frame Prediction

Author

Dai, Kuai, Li, Xutao, Ye, Yunming, Wang, Yaowei, Feng, Shanshan, and Xian, Di

Abstract

Long-term spatial-temporal frame prediction focuses on predicting future image frames precisely, which has numerous applications in real-world scenarios. Existing deep learning prediction models mainly rely on advanced neural network architectures to model complicated spatial-temporal features, which make few efforts to explore high-order correlations to better capture long-term dynamics. Their prediction on long-term frames suffers from inaccurate visual and motion detail issue. In this article, we propose a high-order prediction model for long-term frame prediction, which improves the appearance and motion details by designing special high-order correlation modules in two aspects. First, to enhance the appearance details of predicted frames, we propose a high-order appearance encoder module, where high-order appearance features can be effectively captured with a carefully designed Non-local ConvLSTM. Second, to guarantee the motion accuracy of predicted sequences, we carefully design a high-order motion encoder module, which can accurately capture and preserve the high-order motion patterns with adaptive motion extractors and progressive memory banks, respectively. Comprehensive experiments are conducted on six challenging datasets from real-world scenarios, which demonstrate the effectiveness and superiority of our proposed method over state-of-the-art methods.

Published
2024
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90. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Author

Liu, Longqi, Liu, Chuanyu, Quintero, Andrés, Wu, Liang, Yuan, Yue, Wang, Mingyue, Cheng, Mengnan, Leng, Lizhi, Xu, Liqin, Dong, Guoyi, Li, Rui, Liu, Yang, Wei, Xiaoyu, Xu, Jiangshan, Chen, Xiaowei, Lu, Haorong, Chen, Dongsheng, Wang, Quanlei, Zhou, Qing, Lin, Xinxin, Li, Guibo, Liu, Shiping, Wang, Qi, Wang, Hongru, Fink, J. Lynn, Gao, Zhengliang, Liu, Xin, Hou, Yong, Zhu, Shida, Yang, Huanming, Ye, Yunming, Lin, Ge, Chen, Fang, Herrmann, Carl, Eils, Roland, Shang, Zhouchun, and Xu, Xun

Published
2019
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91. MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

Author

Zhang, Baoquan, Li, Xutao, Feng, Shanshan, Ye, Yunming, and Ye, Rui

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, General Medicine
Abstract

Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predicting novel classes via a cosine nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. To this end, we propose a novel meta-learning based prototype optimization framework to rectify prototypes, \emph{i.e.}, introducing a meta-optimizer to optimize prototypes. Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, \emph{i.e.}, the mean-based gradient estimation is also biased on sparse data. To address the issue, we regard the gradient and its flow as meta-knowledge and then propose a novel Neural Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes, called MetaNODE. In this meta-optimizer, we first view the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural ODE. A gradient flow inference network is carefully designed to learn to estimate the continuous gradient flow for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE. Extensive experiments on miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our method., Accepted by AAAI 2022

Published
2022

92. Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature.

Author

Zhang, Bowen, Fu, Xianghua, Luo, Chuyao, Ye, Yunming, Li, Xutao, and Jing, Liwen

Abstract

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Published
2023
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