標(biāo)題: Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat [打印本頁] 作者: fasten 時間: 2025-3-21 19:16
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
書目名稱Neural Information Processing年度引用
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
作者: FOR 時間: 2025-3-21 21:01 作者: instate 時間: 2025-3-22 01:07 作者: 和藹 時間: 2025-3-22 06:00 作者: 脫毛 時間: 2025-3-22 10:48 作者: 付出 時間: 2025-3-22 16:20
Xingyu Chen,Fanghui Liu,Enmei Tu,Longbing Cao,Jie Yanghe concurrent execution of test cases in the cloud, and a series of case studies illustrating the use of the framework and the environment. Experimental results indicate a significant reduction in test execution time is possible when compared with a typical sequential environment..Software testing i作者: 冥界三河 時間: 2025-3-22 20:10 作者: 一再困擾 時間: 2025-3-22 23:51 作者: 疲勞 時間: 2025-3-23 03:38 作者: 茁壯成長 時間: 2025-3-23 08:59 作者: Mediocre 時間: 2025-3-23 10:04 作者: accrete 時間: 2025-3-23 15:15
Sihui Luo,Yezhou Yang,Yanling Yin,Chengchao Shen,Ya Zhao,Mingli Song作者: Pituitary-Gland 時間: 2025-3-23 21:08
Huafeng Wu,Yawen Wu,Liyan Sun,Congbo Cai,Yue Huang,Xinghao Ding作者: 推測 時間: 2025-3-24 02:17 作者: Arroyo 時間: 2025-3-24 06:09 作者: 天真 時間: 2025-3-24 10:23
velopment lifecycle.Leverage test code across the organizati.Software leaders, directors, and managers of all types need to know about software testing.? It can be a tough climb up the mountain of technical jargon. Engineers seem to be speaking a language all their own sometimes.? Most books on test作者: Figate 時間: 2025-3-24 14:02 作者: LAP 時間: 2025-3-24 17:34 作者: 新娘 時間: 2025-3-24 19:58 作者: 欄桿 時間: 2025-3-25 02:59 作者: 滔滔不絕地說 時間: 2025-3-25 05:00 作者: 出價 時間: 2025-3-25 11:23
Co-consistent Regularization with Discriminative Feature for Zero-Shot Learningriminative feature extraction, we propose an end-to-end framework, which is different from traditional ZSL methods in the following two aspects: (1) we use a cascaded network to automatically locate discriminative regions, which can better extract latent features and contribute to the representation作者: 匍匐前進 時間: 2025-3-25 14:42
Hybrid Networks: Improving Deep Learning Networks via Integrating Two Views of Imagesata by transforming it into column vectors which destroys its spatial structure while obtaining the principal components. In this research, we first propose a tensor-factorization based method referred as the . (.). The . retains the spatial structure of the data by preserving its individual modes. 作者: acetylcholine 時間: 2025-3-25 18:36
On a Fitting of a Heaviside Function by Deep ReLU Neural Networksd an advantage of a deep structure in realizing a heaviside function in training. This is significant not only as simple classification problems but also as a basis in constructing general non-smooth functions. A heaviside function can be well approximated by a difference of ReLUs if we can set extr作者: 叢林 時間: 2025-3-25 22:37 作者: Scintillations 時間: 2025-3-26 03:45
Efficient Integer Vector Homomorphic Encryption Using Deep Learning for Neural Networksosing users’ privacy when we train a high-performance model with a large number of datasets collected from users without any protection. To protect user privacy, we propose an Efficient Integer Vector Homomorphic Encryption (EIVHE) scheme using deep learning for neural networks. We use EIVHE to encr作者: rods366 時間: 2025-3-26 05:36 作者: 你敢命令 時間: 2025-3-26 09:42
Multi-stage Gradient Compression: Overcoming the Communication Bottleneck in Distributed Deep Learniaining. Gradient compression is an effective way to relieve the pressure of bandwidth and increase the scalability of distributed training. In this paper, we propose a novel gradient compression technique, Multi-Stage Gradient Compression (MGC) with Sparsity Automatic Adjustment and Gradient Recessi作者: CROW 時間: 2025-3-26 15:01 作者: 才能 時間: 2025-3-26 20:24 作者: BOGUS 時間: 2025-3-26 20:59
Deep Collaborative Filtering Combined with High-Level Feature Generation on Latent Factor Modell feature playing on semantic factor cases. However, in more common scenes where semantic features cannot be reached, research involving high-level feature on latent factor models is lacking. Analogizing to the idea of the convolutional neural network in image processing, we proposed a Weighted Feat作者: 打折 時間: 2025-3-27 01:57
Data Imputation of Wind Turbine Using Generative Adversarial Nets with Deep Learning Models affect the safety of power system and cause economic loss. However, under some complicated conditions, the WT data changes according to different environments, which would reduce the efficiency of some traditional data interpolation methods. In order to solve this problem and improve data interpola作者: MEN 時間: 2025-3-27 07:13
A Deep Ensemble Network for Compressed Sensing MRIptimization based CS-MRI methods lack enough capacity to encode rich patterns within the MR images and the iterative optimization for sparse recovery is often time-consuming. Although the deep convolutional neural network (CNN) models have achieved the state-of-the-art performance on CS-MRI reconstr作者: 誘導(dǎo) 時間: 2025-3-27 12:16 作者: Legion 時間: 2025-3-27 15:22 作者: 放氣 時間: 2025-3-27 21:28
Understanding Deep Neural Network by Filter Sensitive Area Generation Network clear why they achieve such great success. In this paper, a novel approach called Filter Sensitive Area Generation Network (FSAGN), has been proposed to interpret what the convolutional filters have learnt after training CNNs. Given any trained CNN model, the proposed method aims to figure out whic作者: tympanometry 時間: 2025-3-27 23:17
Deep-PUMR: Deep Positive and Unlabeled Learning with Manifold Regularizationationship of positive and unlabeled examples; (ii) The adopted deep network enables Deep-PUMR with strong learning ability, especially on large-scale datasets. Extensive experiments on five diverse datasets demonstrate that Deep-PUMR achieves the state-of-the-art performance in comparison with classic PU learning algorithms and risk estimators.作者: 河潭 時間: 2025-3-28 03:52 作者: Badger 時間: 2025-3-28 08:18 作者: inchoate 時間: 2025-3-28 11:25
Multi-stage Gradient Compression: Overcoming the Communication Bottleneck in Distributed Deep Learniession ratio up?to 3800x without incurring accuracy loss. We compress gradient size of ResNet-50 from 97?MB to 0.03?MB, for AlexNet from 233?MB to 0.06?MB. We even get a better accuracy than baseline on GoogLeNet. Experiments also show the significant scalability of MGC.作者: offense 時間: 2025-3-28 15:39
Teach to Hash: A Deep Supervised Hashing Framework with Data Selection update the training set with the most effective ones. Experimental results on two typical image datasets indicate that the introduced “teacher” can significantly improve the performance of deep hashing framework and the proposed method outperforms the state-of-the-art hashing methods.作者: restrain 時間: 2025-3-28 22:17 作者: AMITY 時間: 2025-3-28 22:57 作者: 下邊深陷 時間: 2025-3-29 05:16
Understanding Deep Neural Network by Filter Sensitive Area Generation Networkter mask operation. Experiments on multiple datasets and networks show that FSAGN clarifies the knowledge representations of each filter and how small disturbance on specific object parts affects the performance of CNNs.作者: 預(yù)感 時間: 2025-3-29 10:15 作者: 嚴(yán)厲批評 時間: 2025-3-29 14:54 作者: neurologist 時間: 2025-3-29 19:02
Multi-view Deep Gaussian Processesexible and powerful. In contrast with the DGPs, MvDGPs support asymmetrical modeling depths for different view of data, resulting in better characterizations of the discrepancies among different views. Experimental results on multiple multi-view data sets have verified the flexibilities and effectiveness of the proposed model.作者: resistant 時間: 2025-3-29 20:19 作者: 分發(fā) 時間: 2025-3-30 03:18
0302-9743 ,?ICONIP 2018, held in Siem Reap, Cambodia, in December 2018..The 401?full papers presented were carefully?reviewed and selected from 575 submissions. The papers?address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques acros作者: BOAST 時間: 2025-3-30 06:52 作者: 消散 時間: 2025-3-30 09:13
0302-9743 s different domains.?The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural networks, recurrent neural networks, and spiking neural networks..978-3-030-04166-3978-3-030-04167-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: faucet 時間: 2025-3-30 13:50 作者: Addictive 時間: 2025-3-30 17:12
Conference proceedings 2018018, held in Siem Reap, Cambodia, in December 2018..The 401?full papers presented were carefully?reviewed and selected from 575 submissions. The papers?address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across differen作者: 抱怨 時間: 2025-3-30 21:26
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663612.jpg作者: RADE 時間: 2025-3-31 03:43
https://doi.org/10.1007/978-3-030-04167-0artificial intelligence; biomedical engineering; data mining; deep learning; hci; human-computer interact作者: 貨物 時間: 2025-3-31 06:15
978-3-030-04166-3Springer Nature Switzerland AG 2018作者: 骨 時間: 2025-3-31 10:43 作者: liaison 時間: 2025-3-31 14:16
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