用户名  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: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

[复制链接]
查看: 14475|回复: 65
发表于 2025-3-21 19:58:06 | 显示全部楼层 |阅读模式
书目名称Machine Learning and Knowledge Discovery in Databases. Research Track
副标题European Conference,
编辑Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano
描述.The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. .The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions...The volumes are organized in topical sections as follows:..Research Track:..Part I:. Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications...Part II:. Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety...Part III: .Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics...Applied Data Science Track:..Part IV:. Anomaly detection and malware; spatio-temporal data; e-commerce and finance; health
出版日期Conference proceedings 2021
关键词applied computing; computer vision; computing methodologies; correlation analysis; data mining; databases
版次1
doihttps://doi.org/10.1007/978-3-030-86523-8
isbn_softcover978-3-030-86522-1
isbn_ebook978-3-030-86523-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 21:36:23 | 显示全部楼层
发表于 2025-3-22 03:48:42 | 显示全部楼层
发表于 2025-3-22 06:42:53 | 显示全部楼层
Joslim: ,oint Widths and Weights ,ptimization for ,mable Neural Networks. From a practical standpoint, we propose Joslim, an algorithm that jointly optimizes both the widths and weights for slimmable nets, which outperforms existing methods for optimizing slimmable networks across various networks, datasets, and objectives. Quantitatively, improvements up to 1.7% and 8%
发表于 2025-3-22 09:45:32 | 显示全部楼层
发表于 2025-3-22 15:41:26 | 显示全部楼层
发表于 2025-3-22 20:08:06 | 显示全部楼层
发表于 2025-3-22 21:45:45 | 显示全部楼层
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Variance Reduced Stochastic Proximal algorithm for AUC Maximization (.) that combines the two areas of analyzing non-decomposable performance metrics with and optimization efforts to guarantee faster convergence. We perform an in-depth theoretical and empirical analysis to demonstrate that our algo
发表于 2025-3-23 01:50:57 | 显示全部楼层
More General and Effective Model Compression via an Additive Combination of Compressionsusing only 1 bit per weight without error degradation at the cost of adding a few floating point weights. However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
发表于 2025-3-23 05:36:54 | 显示全部楼层
 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, 2025-6-13 08:17
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