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: Artificial Intelligence and Soft Computing; 19th International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2020

[复制链接]
楼主: 烈酒
发表于 2025-3-28 18:29:06 | 显示全部楼层
Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flowd after the bubble. We show that without any retraining, the proposed model is temporally stable even as Bitcoin trading shifts into an extremely volatile “bubble trouble” period. The significance of the result is shown by benchmarking against existing state-of-the-art models in the literature for modelling price formation using deep learning.
发表于 2025-3-28 19:36:48 | 显示全部楼层
Application of Neural Networks and Graphical Representations for Musical Genre Classificationgnized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions.
发表于 2025-3-29 02:19:20 | 显示全部楼层
0302-9743 nce and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020..The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts: ​neural networks and their applications;
发表于 2025-3-29 04:29:43 | 显示全部楼层
发表于 2025-3-29 09:52:17 | 显示全部楼层
Fundamental Theories of Physicswhich result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performance of our method and the classic CG method was compared.
发表于 2025-3-29 11:55:50 | 显示全部楼层
Monoranjan Maiti,Samir Maity,Arindam Roy that scientists already exhibited that both systems exhibit almost the same behavior dynamics (chaotic regimes etc.), researchers still take both classes of algorithms as two different classes. We show in this paper, that there are some similarities, that can help to understand evolutionary algorithms as neural networks and vice versa.
发表于 2025-3-29 15:55:14 | 显示全部楼层
发表于 2025-3-29 23:32:11 | 显示全部楼层
Fast Conjugate Gradient Algorithm for Feedforward Neural Networkswhich result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performance of our method and the classic CG method was compared.
发表于 2025-3-30 02:39:15 | 显示全部楼层
发表于 2025-3-30 07:23:59 | 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, 2025-5-26 09:45
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