This paper revisits the principle of uniform convergence in statistical learning, discusses how it acts as the foundation behind machine learning, and attempts to gain a better understanding of the essential problem that current deep learning algorithms are solving. Using computer vision as an example domain in machine learning, the discussion shows that recent research trends in leveraging increasingly large-scale data to perform pre-training for representation learning are largely to reduce the discrepancy between a practically tractable empirical loss and its ultimately desired but intractable expected loss. Furthermore, this paper suggests a few future research directions, predicts the continued increase of data, and argues that more fundamental research is needed on robustness, interpretability, and reasoning capabilities of machine learning by incorporating structure and knowledge.
翻译:本文回顾了统计学习统一一致的原则,讨论了它如何成为机器学习的基础,并试图更好地了解目前深层学习算法正在解决的基本问题。利用计算机的视野作为机器学习的一个范例,讨论表明,最近利用日益大规模的数据进行代表性学习培训前培训的研究趋势,主要是缩小实际可移植的经验损失与其最终期望但难以解决的预期损失之间的差距。此外,本文件提出了未来一些研究方向,预测了数据的持续增加,并论证说,需要通过纳入结构和知识,对机器学习的稳健性、可解释性和推理能力进行更根本的研究。