Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

BOGUS

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

打折

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

MEN

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

誘導(dǎo)

Legion

放氣

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

tympanometry

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.

河潭

Badger

inchoate

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.