Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.

翻译：神经网络权重的初始化对其性能起着决定性作用。特征模仿网络（FINs）提供了一种新颖策略，通过初始化权重以近似特定闭式统计特征，为深度学习架构奠定了坚实基础。尽管FINs的适用性此前主要在生物医学领域得到验证，本研究将其探索扩展至其他时间序列数据集。通过三项独立实验，本文测试了模仿Tsallis熵对性能提升的适用性：比特币价格预测、语音情感识别及慢性颈痛检测。在比特币价格预测任务中，嵌入FINs的模型相比基线将均方根误差降低了约1000。语音情感识别任务中，经FIN增强的模型分类准确率提升了超过3%。最后，在慢性颈痛检测实验中，相较于已有分类器，性能提升约7%。这些发现验证了FINs在多样化应用中的广泛效用与潜力。