.png){kind=icon}
返回
GAR: An efficient and scalable graph-based activity regularization for semi-supervised learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2018.03.028.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training. Adjacency of the examples is inferred using the predictions of a neural network model which is first initialized by a supervised pretraining. These predictions are then updated according to a novel unsupervised objective which regularizes another adjacency, now linking the output nodes. Regularizing the adjacency of the output nodes, inferred from the predictions of the network, creates an easier optimization problem and ultimately provides that the predictions of the network turn into the optimal embedding. Ultimately, the proposed framework provides an effective and scalable graph-based solution which is natural to the operational mechanism of deep neural networks. Our results show comparable performance with state-of-the-art generative approaches for semi-supervised learning on an easier-to-train, low-cost framework. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Semi-supervised learning
Neural networks
Graph-based
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Effects of stereoisomers of estradiol on food intake, body weight and hoarding behavior in female rats雌二醇立体异构体对雌性大鼠摄食、体重和囤积行为的影响
PREAI
分享收藏A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid电动汽车充电站对配电网的技术影响研究综述
PREAI
分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容