Titlebook: Neural Information Processing; 23rd International C Akira Hirose,Seiichi Ozawa,Derong Liu Conference proceedings 2016 Springer Internationa

Gullet

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

Horizon

祖先

FOLLY

Semi Supervised Autoencoder(greedily) construct a stacked architecture. We demonstrate the efficacy our design in terms of both accuracy and run time requirements for the case of image classification. Our model is able to provide high classification accuracy with even simple classification schemes as compared to existing models for deep architectures.

令人作嘔

Sampling-Based Gradient Regularization for Capturing Long-Term Dependencies in Recurrent Neural Netwframework experimentally we use some special synthetic benchmarks for testing RNNs on ability to capture long-term dependencies. Our network can detect links between events in the (temporal) sequence at the range?100 and longer.

間接

Modal Regression via Direct Log-Density Derivative Estimation good density estimator does not necessarily mean a good density derivative estimator. In this paper, we propose a novel method for modal regression based on . estimation of the log-density derivative without density estimation. Experiments show the superiority of our direct method over PMS.

嚴(yán)峻考驗(yàn)

保留

Nuclear Norm Regularized Randomized Neural Networkhidden layer to target) network. This is solved by imposing a nuclear norm penalty. The proposed technique is compared with the basic ELM and the Sparse ELM. Results on benchmark datasets, show that our method outperforms both of them.

小教堂

A Theoretical Analysis of Semi-supervised Learningtively different dynamical behaviors. Furthermore, we propose a new algorithm that improves the generalization performance by switching the number of input vectors used in an update as the time step proceeds.

睨視