Search Results for author: Yuxing Peng

Found 15 papers, 11 papers with code

Geometric Transformer for Fast and Robust Point Cloud Registration

1 code implementation14 Feb 2022 Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kai Xu

Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.

Point Cloud Registration

Audio Tagging by Cross Filtering Noisy Labels

no code implementations16 Jul 2020 Boqing Zhu, Kele Xu, Qiuqiang Kong, Huaimin Wang, Yuxing Peng

Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings.

Audio Tagging Multi-Task Learning

A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

1 code implementation IJCNLP 2019 Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li

Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.

Question Answering Reading Comprehension

ThunderNet: Towards Real-time Generic Object Detection

4 code implementations28 Mar 2019 Zheng Qin, Zeming Li, Zhaoning Zhang, Yiping Bao, Gang Yu, Yuxing Peng, Jian Sun

In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet.

Object Detection

Attention-Guided Answer Distillation for Machine Reading Comprehension

no code implementations EMNLP 2018 Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, Ming Zhou

Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models.

Knowledge Distillation Machine Reading Comprehension

Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions

3 code implementations27 Mar 2018 Zheng Qin, Zhaoning Zhang, Dongsheng Li, Yiming Zhang, Yuxing Peng

Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds.

Learning Environmental Sounds with Multi-scale Convolutional Neural Network

1 code implementation25 Mar 2018 Boqing Zhu, Changjian Wang, Feng Liu, Jin Lei, Zengquan Lu, Yuxing Peng

For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce two-phase method to fuse the different features.

Sound Audio and Speech Processing

Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications

2 code implementations24 Mar 2018 Zheng Qin, Zhaoning Zhang, Shiqing Zhang, Hao Yu, Yuxing Peng

Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications.

FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy

2 code implementations11 Feb 2018 Zheng Qin, Zhaoning Zhang, Xiaotao Chen, Yuxing Peng

Experiments on ILSVRC 2012 and PASCAL VOC 2007 datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5. 5% on the ILSVRC 2012 top-1 accuracy and 3. 6% on the VOC 2007 mAP under a complexity of 12 MFLOPs.

Reinforced Mnemonic Reader for Machine Reading Comprehension

3 code implementations8 May 2017 Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.

Machine Reading Comprehension Question Answering +1

hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification

no code implementations31 Aug 2016 Ting-Ting Xie, Yuxing Peng, Changjian Wang

Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased.

General Classification Incremental Learning

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