实验室研究员在CCF推荐 国际知名学术期刊Neural Processing Letters上发表文章Hierarchical Task-Incremental Learning withInspired by[PDF] 1.Instruction 一个好的终身学习模型能够随着时间的推移不断地更新其概念词汇,这与人类不断的完善和扩展其对概念层次的理解的方式如出一辙。举例来说,人类最初学习“猫”这一一般...
实验室研究员在国际知名学术期刊Neural Processing Letters上发表文章，文章标题为"Hierarchical Task-Incremental Learning with Feature-Space Initialization Inspired by Neural Collapse"。此文章旨在探讨如何构建一个能随时间更新概念词汇的终身学习模型，以类比人类对概念层次的理解。文章指出，现有许多增量学习...
期刊全称 Neural Processing Letters 期刊简称 NPL Print ISSN 1370-4621 Online ISSN 1573-773X 期刊出版社 Springer 是否开放获取 Open Access,OA 否官网地址 期刊所属领域 人工智能 期刊简介 神经处理快报(Neural Processing Letters,NPL)期刊专注发表关于人工神经网络和机器学习系统各方面的技术文章。
计算机科学1-4区都有SCI王牌水刊 | 4区水刊: 🔹《Computer Journal》 IF=1.5,年文章量147篇。非OA,CCFB老牌期刊 🔹《NEURAL PROCESSING LETTERS》 IF=2.6,年文章量约317篇。0A,Springer出版,专注计算机科学中人工智能领域,包括理论开发、生物模型、学习、应用、软件和硬件开发等。发文量稳增,收文章类型多,...
影响因子: 2.600 出版商: Springer ISSN: 1370-4621 浏览: 19860 关注: 20 征稿 Aims and scope Neural Processing Letters is an international journal that promotes fast exchange of the current state-of-the art contributions among the artificial neural network community of researchers and users. The Jou...
Neural Processing Letters Aims and scope Submit manuscript Heng Wang & Yunlong Yu 482 Accesses 9 Citations Explore all metrics Abstract The rapid development of remote sensing technology let us acquire a large collection of remote sensing scene images with high resolution. Aerial scene classification...
In recent years, deep learning has achieved great success in many natural language processing tasks, including named entity recognition. The shortcoming is that a large quantity of manually annotated data is usually required. Previous studies have demonstrated that active learning can considerably reduce...
CCFCOREQUALIS简称全称截稿日期通知日期会议日期 ba*b1IJCARInternational Joint Conference on Automated Reasoning2024-01-292024-03-282024-07-01 MINDInternational Conference on Machine Learning, Image Processing, Networks and Data Sciences2018-12-152018-12-302019-03-03 ...
摘要原文 In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium/highlevel features extraction. We make use of ConvNets...
• Multi-Step NaiveCNN (NaiveCNN-MS) which use a 2D CNN to extract visual feature from each depth image and fuse them together to get the overall visual feature, instead of stacking multiple depth images and processing the stacked visual observation with a 2D CNN. For NaiveCNN...