Search Results for author: Fan Xu

Found 15 papers, 3 papers with code

“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)

no code implementations CCL 2020 Bailian Qiu, Mingwen Wang, Maoxi Li, Cong Chen, Fan Xu

机器翻译错误分析旨在找出机器译文中存在的错误, 包括错误类型、错误分布等, 它在机器翻译研究和应用中起着重要作用。该文将人工译后编辑与错误分析结合起来, 对译后编辑操作进行错误标注, 采用自动标注和人工标注相结合的方法, 构建了一个细粒度英汉机器翻译错误分析语料库, 其中每一个标注样本包括源语言句子、机器译文、人工参考译文、译后编辑译文、词错误率和错误类型标注;标注的错误类型包括增词、漏词、错词、词序错误、未译和命名实体翻译错误等。标注的一致性检验表明了标注的有效性;对标注语料的统计分析结果能有效地指导机器翻译系统的开发和人工译员的后编辑。

Machine Translation

Fuzzy Positive Learning for Semi-supervised Semantic Segmentation

no code implementations16 Oct 2022 Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang, Guoli Song, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.

Semi-Supervised Semantic Segmentation

$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features

no code implementations6 Jul 2022 Zhennan Wang, Kehan Li, Runyi Yu, Yian Zhao, Pengchong Qiao, Fan Xu, Guoli Song, Jie Chen

In this paper, we show that the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.

Acoustic Scene Classification Image Classification +1

Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach

no code implementations28 Jun 2022 Naifu Zhang, Meixia Tao, Jia Wang, Fan Xu

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters.

Model Compression Quantization

CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

1 code implementation28 Jun 2022 Fan Xu, Yunxiang Zhang, Xiaojun Wan

Chinese character riddle is a challenging riddle game which takes a single character as the solution.

Question Answering

A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks

no code implementations1 Jun 2021 Qihui Wu, Tianchen Ruan, Fuhui Zhou, Yang Huang, Fan Xu, Shijin Zhao, Ya Liu, Xuyang Huang

Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals.


X-ray Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology

no code implementations10 Dec 2020 Fan Xu, Chen-Han Tang, Jin-Jun Geng, Fa-Yin Wang, Yu-Yang Wang, Abudushataer Kuerban, Yong-Feng Huang

After correcting for the redshift evolution, we show that the de-evolved $L-T-E_{\rm{p}}$ correlation can be used as a standard candle.

High Energy Astrophysical Phenomena

DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

1 code implementation20 May 2018 Yu Li, Fan Xu, Fa Zhang, Pingyong Xu, Mingshu Zhang, Ming Fan, Lihua Li, Xin Gao, Renmin Han

Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.

Bayesian Inference Super-Resolution +2

An entity-driven recursive neural network model for chinese discourse coherence modeling

no code implementations14 Apr 2017 Fan Xu, Shujing Du, Maoxi Li, Mingwen Wang

Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing field. Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated features to capture the logic or syntactic or semantic relationships acrosssentences within a text. In this paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation based on current English discourse coherenceneural network model.

Coherence Evaluation Feature Engineering +3

Sentence-level dialects identification in the greater China region

no code implementations8 Jan 2017 Fan Xu, Mingwen Wang, Maoxi Li

Identifying the different varieties of the same language is more challenging than unrelated languages identification.

Word Alignment

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