论文标题
神经元的多样性可以改善物理及以后的机器学习
Neuronal diversity can improve machine learning for physics and beyond
论文作者
论文摘要
多样性传达了自然界的优势，但是均匀的神经元通常构成了人工神经网络的层次。在这里，我们从神经元中构建神经网络，这些神经元可以学习自己的激活功能，迅速多样化，随后在图像分类和非线性回归任务上优于其同质对应物。子网络实例化神经元，该神经元特别有效的非线性反应集。例如，传统的神经网络对数字进行分类和预测范德波尔振荡器和物理知识的汉密尔顿神经网络学习Hénon-Heiles Stellar Orbits以及视频记录的摆钟的挥杆。这样的\ textit {学习的多样性}提供了动态系统的示例，这些系统选择了多样性而不是均匀性，并阐明了多样性在自然和人造系统中的作用。
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning Hénon-Heiles stellar orbits and the swing of a video recorded pendulum clock. Such \textit{learned diversity} provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.
