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Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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發(fā)表于 2025-3-21 19:01:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Neural Information Processing
副標(biāo)題26th International C
編輯Tom Gedeon,Kok Wai Wong,Minho Lee
視頻videohttp://file.papertrans.cn/664/663641/663641.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla
描述The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; computer networks; computer systems; computer vision; deep learning; education; e
版次1
doihttps://doi.org/10.1007/978-3-030-36808-1
isbn_softcover978-3-030-36807-4
isbn_ebook978-3-030-36808-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




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書目名稱Neural Information Processing年度引用學(xué)科排名




書目名稱Neural Information Processing讀者反饋




書目名稱Neural Information Processing讀者反饋學(xué)科排名




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A Deep Learning Scheme for Extracting Pedestrian-Parcel Tuples from Videosy re-identification of pedestrians and parcels. In the interaction module, we propose a lightweight interaction model for discriminating the affiliation between pedestrians and parcels in a single RGB image. Experiments on a video data at a subway entrance validate the proposed approach.
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Fault Tolerant Broad Learning Systemtolerant BLS (FTBLS). First, we develop a fault tolerant objective function for BLS. Based on the developed objective function, we develop a training algorithm to construct a BLS network. The simulation results show that our proposed FTBLS is much better than the classical BLS.
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