Multi-dimensional ability diagnosis for machine learning algorithms

被引：0

作者：

Qi LIU ^{[1
]}

Zheng GONG ^{[1
]}

Zhenya HUANG ^{[1
]}

Chuanren LIU ^{[2
]}

Hengshu ZHU ^{[3
]}

Zhi LI ^{[4
]}

Enhong CHEN ^{[1
]}

Hui XIONG ^{[5
]}

机构：

[1] State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China

[2] Business Analytics and Statistics, The University of Tennessee

[3] Computer Network Information Center, Chinese Academy of Sciences

[4] Shenzhen International Graduate School, Tsinghua University

[5] Hong Kong University of Science and Technology

来源：

Science China(Information Sciences) | 2024年 / 67卷 / 12期

关键词：

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

摘要：

<正>A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.

引用

页码：321 / 322

页数：2

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