Search Results for author: Bin Guo

Found 14 papers, 1 papers with code

AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

no code implementations11 Oct 2021 Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, Zhiwen Yu

On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.

Community Question Answering

Multiway sparse distance weighted discrimination

1 code implementation11 Oct 2021 Bin Guo, Lynn E. Eberly, Pierre-Gilles Henry, Christophe Lenglet, Eric F. Lock

We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure.


DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction

no code implementations18 Aug 2021 Siyuan Ren, Bin Guo, Longbing Cao, Ke Li, Jiaqi Liu, Zhiwen Yu

To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.

TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples

no code implementations16 Aug 2021 Jiahui Cheng, Bin Guo, Jiaqi Liu, Sicong Liu, Guangzhi Wu, Yueqi Sun, Zhiwen Yu

To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.

Defect Detection Transfer Learning

AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications

no code implementations28 Jan 2021 Sicong Liu, Bin Guo, Ke Ma, Zhiwen Yu, Junzhao Du

There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.

Model Compression

Towards information-rich, logical text generation with knowledge-enhanced neural models

no code implementations2 Mar 2020 Hao Wang, Bin Guo, Wei Wu, Zhiwen Yu

Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.

Text Generation

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

no code implementations21 Jan 2020 Kaixuan Chen, Dalin Zhang, Lina Yao, Bin Guo, Zhiwen Yu, Yunhao Liu

In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.

Activity Recognition

The Future of Misinformation Detection: New Perspectives and Trends

no code implementations9 Sep 2019 Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu

We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.


Conditional Text Generation for Harmonious Human-Machine Interaction

no code implementations8 Sep 2019 Bin Guo, Hao Wang, Yasan Ding, Wei Wu, Shaoyang Hao, Yueqi Sun, Zhiwen Yu

In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication.

Conditional Text Generation

Multi-agent Attentional Activity Recognition

no code implementations22 May 2019 Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu

And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.

Activity Recognition

AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions

no code implementations1 May 2019 Qiuyun Zhang, Bin Guo, Hao Wang, Yunji Liang, Shaoyang Hao, Zhiwen Yu

In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning.

Text Generation

Smart City Development with Urban Transfer Learning

no code implementations5 Aug 2018 Leye Wang, Bin Guo, Qiang Yang

To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.

Transfer Learning

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