Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1447-1454.DOI: 10.11772/j.issn.1001-9081.2021030516
• Data science and technology • Previous Articles Next Articles
Ying CHEN1, Jiong YU1,2(), Jiaying CHEN2, Xusheng DU2
Received:
2021-04-06
Revised:
2021-06-22
Accepted:
2021-06-22
Online:
2022-06-11
Published:
2022-05-10
Contact:
Jiong YU
About author:
CHEN Ying, born in 1999,M. S. candidate. Her research interestsinclude data mining,machine learning.Supported by:
通讯作者:
于炯
作者简介:
陈颖(1999—),女,湖南娄底人,硕士研究生,主要研究方向:数据挖掘、机器学习基金资助:
CLC Number:
Ying CHEN, Jiong YU, Jiaying CHEN, Xusheng DU. Cross-layer data sharing based multi-task model[J]. Journal of Computer Applications, 2022, 42(5): 1447-1454.
陈颖, 于炯, 陈嘉颖, 杜旭升. 基于交叉层级数据共享的多任务模型[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1447-1454.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030516
数据集 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|
合成数据1 | 1 000 000 | 100 000 | 100 000 |
合成数据2 | 100 000 | 10 000 | 10 000 |
Tab. 1 Synthetic datasets used in experiments
数据集 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|
合成数据1 | 1 000 000 | 100 000 | 100 000 |
合成数据2 | 100 000 | 10 000 | 10 000 |
数据集 | 总样本数 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|---|
UCI census-income | 299 285 | 199 523 | 49 881 | 49 881 |
MovieLens | 100 000 | 70 000 | — | 30 000 |
Tab. 2 Real datasets used in experiments
数据集 | 总样本数 | 训练集样本数 | 验证集样本数 | 测试集样本数 |
---|---|---|---|---|
UCI census-income | 299 285 | 199 523 | 49 881 | 49 881 |
MovieLens | 100 000 | 70 000 | — | 30 000 |
模型 | Task1-Income | Task2-Marital | MCV-AUC | MCV-F1 | MCV-ACC | ||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1-score | ACC | AUC | F1-score | ACC | ||||
Single-task | 0.932 5 | 0.693 1 | 0.952 0 | 0.970 8 | 0.927 0 | 0.928 3 | — | — | — |
Shared-bottom | 0.904 9 | 0.643 6 | 0.845 1 | 0.974 2 | 0.931 3 | 0.932 7 | 1.879 1 | 1.574 9 | 1.777 8 |
Cross-stitch | 0.929 4 | 0.742 3 | 0.950 5 | 0.984 3 | 0.933 4 | 0.934 5 | 1.913 7 | 1.675 7 | 1.885 0 |
PLE | 0.941 5 | 0.713 9 | 0.950 9 | 0.980 6 | 0.927 2 | 0.929 0 | 1.922 1 | 1.641 1 | 1.879 9 |
MMOE | 0.939 3 | 0.679 0 | 0.948 2 | 0.984 9 | 0.932 5 | 0.933 6 | 1.924 2 | 1.611 5 | 1.881 8 |
CLS-0 | 0.946 1 | 0.753 4 | 0.953 2 | 0.986 0 | 0.933 5 | 0.934 6 | 1.932 1 | 1.687 8 | 1.887 8 |
CLS | 0.946 8 | 0.757 7 | 0.953 3 | 0.988 7 | 0.944 4 | 0.945 8 | 1.935 5 | 1.702 1 | 1.899 1 |
Tab. 3 Experimental results on UCI census-income dataset
模型 | Task1-Income | Task2-Marital | MCV-AUC | MCV-F1 | MCV-ACC | ||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1-score | ACC | AUC | F1-score | ACC | ||||
Single-task | 0.932 5 | 0.693 1 | 0.952 0 | 0.970 8 | 0.927 0 | 0.928 3 | — | — | — |
Shared-bottom | 0.904 9 | 0.643 6 | 0.845 1 | 0.974 2 | 0.931 3 | 0.932 7 | 1.879 1 | 1.574 9 | 1.777 8 |
Cross-stitch | 0.929 4 | 0.742 3 | 0.950 5 | 0.984 3 | 0.933 4 | 0.934 5 | 1.913 7 | 1.675 7 | 1.885 0 |
PLE | 0.941 5 | 0.713 9 | 0.950 9 | 0.980 6 | 0.927 2 | 0.929 0 | 1.922 1 | 1.641 1 | 1.879 9 |
MMOE | 0.939 3 | 0.679 0 | 0.948 2 | 0.984 9 | 0.932 5 | 0.933 6 | 1.924 2 | 1.611 5 | 1.881 8 |
CLS-0 | 0.946 1 | 0.753 4 | 0.953 2 | 0.986 0 | 0.933 5 | 0.934 6 | 1.932 1 | 1.687 8 | 1.887 8 |
CLS | 0.946 8 | 0.757 7 | 0.953 3 | 0.988 7 | 0.944 4 | 0.945 8 | 1.935 5 | 1.702 1 | 1.899 1 |
1 | CARUANA R. Multitask learning [M]// THRUN S, PRATT L. Learning to Learn. New York: Springer, 1998: 95-133. 10.1007/978-1-4615-5529-2_5 |
2 | 章荪,尹春勇.基于多任务学习的时序多模态情感分析模型[J].计算机应用,2021,41(6):1631-1639. 10.11772/j.issn.1001-9081.2020091416 |
ZHANG S, YIN C Y. Sequential multimodal sentiment analysis model based on multi-task learning [J]. Journal of Computer Applications, 2021, 41(6): 1631-1639. 10.11772/j.issn.1001-9081.2020091416 | |
3 | 姜尧岗,孙晓刚,林云.基于多任务卷积神经网络人脸检测网络的优化加速方法[J].计算机应用,2019,39(S2):59-62. |
JIANG Y G, SUN X G, LIN Y. Optimization acceleration method for face detection network based on multi-task convolutional neural network[J]. Journal of Computer Applications, 2019, 39(S2): 59-62. | |
4 | BANSAL T, BELANGER D, MCCALLUM A. Ask the GRU: multitask learning for deep text recommendations [C]// Proceedings of the 2016 10th ACM Conference on Recommender Systems. New York: ACM, 2016: 107-114. 10.1145/2959100.2959180 |
5 | SHAO C J, FU H M, CHENG P J. Improving one-class recommendation with multi-tasking on various preference intensities [C]// Proceedings of the 2020 14th ACM Conference on Recommender Systems. New York: ACM, 2020: 498-502. 10.1145/3383313.3412224 |
6 | LU Y C, DONG R H, SMYTH B. Why I like it: multi-task learning for recommendation and explanation [C]// Proceedings of the 2018 12th ACM Conference on Recommender Systems. New York: ACM, 2018: 4-12. 10.1145/3240323.3240365 |
7 | TANG H Y, LIU J N, ZHAO M, et al. Progressive Layered Extraction (PLE): a novel Multi-Task Learning (MTL) model for personalized recommendations [C]// Proceedings of the 2020 14th ACM Conference on Recommender Systems. New York: ACM, 2020: 269-278. 10.1145/3383313.3412236 |
8 | MA J Q, ZHAO Z, YI X Y, et al. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts [C]// Proceedings of the 2018 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 1930-1939. 10.1145/3219819.3220007 |
9 | ISHAN M, ABHINAV S, GUPTA A, et al. Cross-stitch networks for multi-task learning [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 3994-4003. 10.1109/cvpr.2016.433 |
10 | RUDER S, BINGEL J, AUGENSTEIN I, et al. Sluice networks: learning what to share between loosely related tasks [EB/OL]. [2021-02-11]. . |
11 | ZHANG Y, YANG Q. An overview of multi-task learning [J]. National Science Review, 2018, 5(1): 30-43. 10.1093/nsr/nwx105 |
12 | JACOBS R A, JORDAN M I, NOWLAN S J, et al. Adaptive mixtures of local experts [J]. Neural Computation, 1991, 3(1): 79-87. 10.1162/neco.1991.3.1.79 |
13 | MA J Q, ZHAO Z, CHEN J L, et al. SNR: sub-network routing for flexible parameter sharing in multi-task learning[C]// Proceedings of the 2019 33rd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2019: 216-223. 10.1609/aaai.v33i01.3301216 |
14 | ZOPH B, LE Q V. Neural architecture search with reinforcement learning [EB/OL]. [2021-02-11]. . |
15 | WANG N, WANG H N, JIA Y L, et al. Explainable recommendation via multi-task learning in opinionated text data [C]// Proceedings of the 2018 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2018: 165-174. 10.1145/3209978.3210010 |
16 | WANG J L, HOI S C H, ZHAO P L, et al. Online multitask collaborative filtering for on-the-fly recommender systems [C]// Proceedings of the 2013 7th ACM Conference on Recommender Systems. New York: ACM, 2013: 237-244. 10.1145/2507157.2507176 |
17 | QIN Z, CHENG Y C, ZHAO Z, et al. Multitask mixture of sequential experts for user activity streams [C]// Proceedings of the 2020 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 3083-3091. 10.1145/3394486.3403359 |
18 | HADASH G, SHALOM O S, OSADCHY R. Rank and rate: multi-task learning for recommender systems [C]// Proceedings of the 2018 12th ACM Conference on Recommender Systems. New York: ACM, 2018: 451-454. 10.1145/3240323.3240406 |
19 | AKHTAR M S, CHAUHAN D S, EKBAL A. A deep multi-task contextual attention framework for multi-modal affect analysis [J]. ACM Transaction on Knowledge Discovery from Data, 2020, 14(3): Article No.32. 10.1145/3380744 |
20 | ZHAO Z, HONG L C, WEI L, et al. Recommending what video to watch next: a multi-task ranking system [C]// Proceedings of the 2019 13th ACM Conference on Recommender Systems. New York: ACM, 2019: 43-51. 10.1145/3298689.3346997 |
21 | ROSENBLATT F. The perceptron: a probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65(6): 386-408. 10.1037/h0042519 |
[1] | Xingyao YANG, Yu CHEN, Jiong YU, Zulian ZHANG, Jiaying CHEN, Dongxiao WANG. Recommendation model combining self-features and contrastive learning [J]. Journal of Computer Applications, 2024, 44(9): 2704-2710. |
[2] | Yu DU, Yan ZHU. Constructing pre-trained dynamic graph neural network to predict disappearance of academic cooperation behavior [J]. Journal of Computer Applications, 2024, 44(9): 2726-2731. |
[3] | Na WANG, Lin JIANG, Yuancheng LI, Yun ZHU. Optimization of tensor virtual machine operator fusion based on graph rewriting and fusion exploration [J]. Journal of Computer Applications, 2024, 44(9): 2802-2809. |
[4] | Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910. |
[5] | Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718. |
[6] | Rui ZHANG, Pengyun ZHANG, Meirong GAO. Self-optimized dual-modal multi-channel non-deep vestibular schwannoma recognition model [J]. Journal of Computer Applications, 2024, 44(9): 2975-2982. |
[7] | Jinjin LI, Guoming SANG, Yijia ZHANG. Multi-domain fake news detection model enhanced by APK-CNN and Transformer [J]. Journal of Computer Applications, 2024, 44(9): 2674-2682. |
[8] | Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974. |
[9] | Hang YANG, Wanggen LI, Gensheng ZHANG, Zhige WANG, Xin KAI. Multi-layer information interactive fusion algorithm based on graph neural network for session-based recommendation [J]. Journal of Computer Applications, 2024, 44(9): 2719-2725. |
[10] | Guanglei YAO, Juxia XIONG, Guowu YANG. Flower pollination algorithm based on neural network optimization [J]. Journal of Computer Applications, 2024, 44(9): 2829-2837. |
[11] | Ying HUANG, Jiayu YANG, Jiahao JIN, Bangrui WAN. Siamese mixed information fusion algorithm for RGBT tracking [J]. Journal of Computer Applications, 2024, 44(9): 2878-2885. |
[12] | Zheyuan SHEN, Keke YANG, Jing LI. Personalized federated learning method based on dual stream neural network [J]. Journal of Computer Applications, 2024, 44(8): 2319-2325. |
[13] | Chunxue ZHANG, Liqing QIU, Cheng’ai SUN, Caixia JING. Purchase behavior prediction model based on two-stage dynamic interest recognition [J]. Journal of Computer Applications, 2024, 44(8): 2365-2371. |
[14] | Tong CHEN, Fengyu YANG, Yu XIONG, Hong YAN, Fuxing QIU. Construction method of voiceprint library based on multi-scale frequency-channel attention fusion [J]. Journal of Computer Applications, 2024, 44(8): 2407-2413. |
[15] | Rui SHI, Yong LI, Yanhan ZHU. Adversarial sample attack algorithm of modulation signal based on equalization of feature gradient [J]. Journal of Computer Applications, 2024, 44(8): 2521-2527. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||