论文标题
智能扮演骰子：随机性对于机器学习至关重要
Intelligence plays dice: Stochasticity is essential for machine learning
论文作者
论文摘要
许多领域将随机性视为提高计算效率的一种方式，同时经常必须权衡准确性。在这篇观点文章中，我们认为随机性在机器学习（ML）中起着根本不同的作用，并且可能是智能系统的关键要素。当我们回顾ML文献时，我们注意到许多ML方法中的随机性特征，使它们具有鲁棒性，可推广性和校准。我们还注意到，从单个神经元的尖峰模式到动物的复杂行为，随机性似乎是突出的。我们讨论了我们认为随机性可能影响ML的未来的讨论。
Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy. In this perspective article, we argue that stochasticity plays a fundamentally different role in machine learning (ML) and is likely a critical ingredient of intelligent systems. As we review the ML literature, we notice that stochasticity features in many ML methods, affording them robustness, generalizability, and calibration. We also note that randomness seems to be prominent in biological intelligence, from the spiking patterns of individual neurons to the complex behavior of animals. We conclude with a discussion of how we believe stochasticity might shape the future of ML.
