**神经网络新突破：标准化层也能提升非线性表达能力**
近日，北京航空航天大学人工智能学院的黄雷副教授团队取得突破性进展，研究发现在神经网络中标准化层也能提升模型的非线性表达能力。这一发现改变了过去对神经网络标准化层功能的传统认知。
传统观念认为，神经网络主要由线性层、非线性层（激活函数）和标准化层三部分组成。其中，非线性层用于增强网络的表达能力，而标准化层则主要为了稳定和加速训练过程，鲜有研究关注其在表达方面的潜力。
但在最新的研究中，黄雷副教授团队发现Layer Normalization具有非线性表达的能力。他们以北京航空航天大学的学生倪云昊、郭宇芯、贾俊龙为首的研究团队指出，标准化层在神经网络中除了加速训练外，可能在表达层面也有重要作用。这一观点颠覆了学界对标准化层功能的固有看法。特别是在Batch Normalization方面，研究认为它在预测阶段实际上可以引入非线性表达。这为神经网络的设计和开发提供了新的视角。
该研究成果由机器之心AIxiv专栏报道，该栏目一直致力于发布全球顶级的学术和技术内容，有效促进了学术交流与传播。黄雷副教授团队的工作无疑为神经网络的研究注入了新的活力。随着研究的深入，未来或许能进一步挖掘标准化层在神经网络中的潜力，推动人工智能领域的发展。该论文通讯作者黄雷副教授的成果页面已提供链接，以供进一步了解和学习。
英语如下：
News Title: “New Breakthrough in Neural Network: Layer Normalization Demonstrates Nonlinear Expression Potential!”
Keywords: Neural Network Breakthrough, Layer Normalization Nonlinear Expression, Huang Lei Team Research
News Content: **New Breakthrough in Neural Network: Layer Normalization Can Also Enhance Nonlinear Expression**
Recently, Professor Huang Lei’s team from the Artificial Intelligence Institute at Beijing University of Aeronautics and Astronautics has made a breakthrough discovery, revealing that layer normalization in neural networks can enhance the model’s nonlinear expression capabilities. This discovery changes the traditional understanding of the function of neural network layer normalization.
It is traditionally believed that neural networks mainly consist of linear layers, nonlinear layers (activation functions), and normalization layers. Among them, nonlinear layers are used to enhance the network’s expression capabilities, while normalization layers mainly serve to stabilize and accelerate the training process. Little research has focused on their potential for expression.
However, in the latest research, Professor Huang Lei’s team discovered that Layer Normalization has the ability of nonlinear expression. The research team, led by students Ni Yunhao, Guo Yuxin, and Jia Junlong from Beijing University of Aeronautics and Astronautics, pointed out that normalization layers in neural networks not only accelerate training but also play an important role in expression. This view has overturned the academic community’s固有看法 on the function of normalization layers. Especially in terms of Batch Normalization, research suggests that it can introduce nonlinear expression during the prediction stage. This provides a new perspective for the design and development of neural networks.
The research results were reported by the Machine Heart AIxiv column, which has been committed to publishing top-level academic and technical content worldwide, effectively promoting academic exchanges and dissemination. Professor Huang Lei’s team’s work has undoubtedly injected new vitality into neural network research. With further research, the potential of normalization layers in neural networks may be further explored in the future, driving the development of the artificial intelligence field. A link to Professor Huang Lei’s research page is provided for further understanding and learning.
【来源】https://www.jiqizhixin.com/articles/2024-07-02-4
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