Search Results for author: Chenyu You

Found 21 papers, 2 papers with code

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

no code implementations29 Apr 2022 Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou

To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.

Conversational Question Answering Transfer Learning

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

1 code implementation3 Jan 2022 Aosong Feng, Chenyu You, Shiqiang Wang, Leandros Tassiulas

We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.

Graph Classification

MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution

no code implementations28 Oct 2021 Chenyu You, Lianyi Han, Aosong Feng, Ruihan Zhao, Hui Tang, Wei Fan

Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.

Frame Graph Attention +2

Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation

no code implementations29 Sep 2021 Haoyu Ma, Yifan Huang, Tianlong Chen, Hao Tang, Chenyu You, Zhangyang Wang, Xiaohui Xie

However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.

Knowledge Distillation

Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering

no code implementations Findings (EMNLP) 2021 Chenyu You, Nuo Chen, Yuexian Zou

In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.

Question Answering Representation Learning

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

no code implementations13 Aug 2021 Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan

However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.

Data Augmentation Image Generation +3

Undistillable: Making A Nasty Teacher That CANNOT teach students

1 code implementation ICLR 2021 Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.

Knowledge Distillation

Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

no code implementations14 May 2021 Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan

In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.

Contrastive Learning Representation Learning +3

Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension

no code implementations20 Dec 2020 Nuo Chen, Fenglin Liu, Chenyu You, Peilin Zhou, Yuexian Zou

To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.

Machine Reading Comprehension

Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering

no code implementations21 Oct 2020 Chenyu You, Nuo Chen, Yuexian Zou

Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.

Audio Signal Processing Conversational Question Answering +2

Knowledge Distillation for Improved Accuracy in Spoken Question Answering

no code implementations21 Oct 2020 Chenyu You, Nuo Chen, Yuexian Zou

However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.

Automatic Speech Recognition Knowledge Distillation +2

Towards Data Distillation for End-to-end Spoken Conversational Question Answering

no code implementations18 Oct 2020 Chenyu You, Nuo Chen, Fenglin Liu, Dongchao Yang, Yuexian Zou

In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.

Automatic Speech Recognition Conversational Question Answering

Layer-Wise Multi-View Decoding for Improved Natural Language Generation

no code implementations16 May 2020 Fenglin Liu, Xuancheng Ren, Guangxiang Zhao, Chenyu You, Xian Wu, Xu sun

While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.

Abstractive Text Summarization Image Captioning +5

Low-Dose CT via Deep CNN with Skip Connection and Network in Network

no code implementations26 Nov 2018 Chenyu You, Linfeng Yang, Yi Zhang, Ge Wang

The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application.

Computed Tomography (CT)

Super-resolution MRI through Deep Learning

no code implementations16 Oct 2018 Qing Lyu, Chenyu You, Hongming Shan, Ge Wang

Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.

Medical Physics

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

no code implementations10 Aug 2018 Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Punam K. Saha, Ge Wang

Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.

Computed Tomography (CT) Image Restoration +1

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 May 2018 Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang

However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.

Computed Tomography (CT) Denoising

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