找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Big Data; 8th CCF Conference, Hong Mei,Weiguo Zhang,Li Wang Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 artificial

[復制鏈接]
樓主: 忠誠
21#
發(fā)表于 2025-3-25 03:50:10 | 只看該作者
,Multi Dimensional Evaluation of Middle School Students’ Physical and Mental Quality and Intelligentollaborative filtering), using Embedding technology and graph convolutional neural network to mine the attributes and interactive relationship features in the data, and then through the fusion of feature vector expressions to achieve personalized exercise program recommendations. The design and impl
22#
發(fā)表于 2025-3-25 07:50:39 | 只看該作者
23#
發(fā)表于 2025-3-25 11:39:18 | 只看該作者
24#
發(fā)表于 2025-3-25 19:48:17 | 只看該作者
Image Compressed Sensing Using Neural Architecture Search,struction algorithms in both running speed and reconstruction quality. However, it is a time-consuming procedure even for an expert to efficiently design a high-performance network for image CS because of various combination of different kernel size and filter number in each layer. In this paper, a
25#
發(fā)表于 2025-3-25 20:05:33 | 只看該作者
26#
發(fā)表于 2025-3-26 03:20:46 | 只看該作者
Rotation-DPeak: Improving Density Peaks Selection for Imbalanced Data, and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overco
27#
發(fā)表于 2025-3-26 05:31:49 | 只看該作者
28#
發(fā)表于 2025-3-26 08:49:45 | 只看該作者
Improving Small-Scale Dataset Classification Performance Through Weak-Label Samples Generated by Insamples, the amount of available training data is always limited (real data). Generative Adversarial Network (GAN) has good performance in generating artificial samples (generated data), the generated samples can be used as supplementary data to make up for the problem of small dataset with small sa
29#
發(fā)表于 2025-3-26 16:20:21 | 只看該作者
30#
發(fā)表于 2025-3-26 18:37:08 | 只看該作者
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-17 13:41
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
宝坻区| 成武县| 石景山区| 潢川县| 宁都县| 芮城县| 阜南县| 东莞市| 新密市| 平舆县| 郎溪县| 汪清县| 徐州市| 苍南县| 疏勒县| 通海县| 左权县| 上饶县| 剑河县| 嘉峪关市| 和田县| 东乡| 休宁县| 互助| 许昌县| 武邑县| 宜州市| 邢台市| 芜湖市| 北票市| 怀柔区| 剑阁县| 闽清县| 永安市| 镇远县| 灵山县| 大兴区| 舒城县| 洱源县| 平度市| 汤原县|