找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Recent Trends in Learning From Data; Tutorials from the I Luca Oneto,Nicolò Navarin,Davide Anguita Book 2020 The Editor(s) (if applicable)

[复制链接]
查看: 8449|回复: 42
发表于 2025-3-21 17:54:53 | 显示全部楼层 |阅读模式
书目名称Recent Trends in Learning From Data
副标题Tutorials from the I
编辑Luca Oneto,Nicolò Navarin,Davide Anguita
视频video
概述Gathers tutorials from the 2019 INNS Big Data and Deep Learning Conference.Describes cutting-edge AI-based tools and applications.Offers essential guidance on the design and analysis of advanced AI-ba
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Recent Trends in Learning From Data; Tutorials from the I Luca Oneto,Nicolò Navarin,Davide Anguita Book 2020 The Editor(s) (if applicable)
描述This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research. 
出版日期Book 2020
关键词Deep Learning for Graphs; Feedforward neural networks; Applications of tensor decomposition; Continual
版次1
doihttps://doi.org/10.1007/978-3-030-43883-8
isbn_softcover978-3-030-43885-2
isbn_ebook978-3-030-43883-8Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Recent Trends in Learning From Data影响因子(影响力)




书目名称Recent Trends in Learning From Data影响因子(影响力)学科排名




书目名称Recent Trends in Learning From Data网络公开度




书目名称Recent Trends in Learning From Data网络公开度学科排名




书目名称Recent Trends in Learning From Data被引频次




书目名称Recent Trends in Learning From Data被引频次学科排名




书目名称Recent Trends in Learning From Data年度引用




书目名称Recent Trends in Learning From Data年度引用学科排名




书目名称Recent Trends in Learning From Data读者反馈




书目名称Recent Trends in Learning From Data读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

1票 100.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 23:34:12 | 显示全部楼层
Introduction,e International Neural Network Society, with the aim of representing an international meeting for researchers and other professionals in Big Data, Deep Learning and related areas. This book collects the tutorials presented at the conference which cover most of the recent trends in learning from data
发表于 2025-3-22 02:18:51 | 显示全部楼层
发表于 2025-3-22 04:35:18 | 显示全部楼层
Deep Randomized Neural Networks,ic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Rando
发表于 2025-3-22 12:26:34 | 显示全部楼层
发表于 2025-3-22 16:18:42 | 显示全部楼层
Deep Learning for Graphs, a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across
发表于 2025-3-22 17:35:45 | 显示全部楼层
Limitations of Shallow Networks,pplications till the recent renewal of interest in deep architectures. Experimental evidence and successful applications of deep networks pose theoretical questions asking: When and why are deep networks better than shallow ones? This chapter presents some probabilistic and constructive results on l
发表于 2025-3-22 21:31:16 | 显示全部楼层
Fairness in Machine Learning,out the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteris
发表于 2025-3-23 03:45:49 | 显示全部楼层
Online Continual Learning on Sequences,usly encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that ad
发表于 2025-3-23 08:07:17 | 显示全部楼层
Book 2020er advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research. 
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-6-11 09:18
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表