2017 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献

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Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach 面向共享集群的分布式机器学习:一种动态分区方法

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2017-04-22 DOI: 10.1109/SMARTCOMP.2017.7947053

Peng Sun, Yonggang Wen, T. Duong, Shengen Yan

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引用次数: 29

CRNN: A Joint Neural Network for Redundancy Detection 一种用于冗余检测的联合神经网络

2017 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2016-04-27 DOI: 10.2139/ssrn.2962196

Xinyu Fu, Eugene Ch’ng, U. Aickelin, S. See

{"title":"CRNN: A Joint Neural Network for Redundancy Detection","authors":"Xinyu Fu, Eugene Ch’ng, U. Aickelin, S. See","doi":"10.2139/ssrn.2962196","DOIUrl":"https://doi.org/10.2139/ssrn.2962196","url":null,"abstract":"This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character- aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the- art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty- news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells' impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

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