论文标题
重新思考监督的学习：从生物学习中的见解，并以其名称呼唤它
Rethinking supervised learning: insights from biological learning and from calling it by its name
论文作者
论文摘要
人工神经网络的复兴是由分类模型的成功催化的，分类模型的成功标记了社区的更广泛的学期监督学习。非凡的结果导致了充满雄心勃勃的诺言和夸大的炒作。很快，社区意识到，成功归功于成千上万个标记的例子和监督学习的可用性，从荣耀到羞耻感。但是，所有这些似乎是品牌名称，而不是理论上扎根的分类法的实际类别。此外，呼吁消除监督学习的呼吁是由人类几乎没有或没有监督学习，并且能够强大的分布概括的可疑说法。在这里，我们回顾了关于自然界学习和监督的见解，重新审视了一个观念，即如果没有监督或归纳偏见，学习和概括是不可能的，并认为如果我们以其名称称呼它，我们将取得更好的进步。
The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave rise to a hype loaded with ambitious promises and overstatements. Soon the community realised that the success owed much to the availability of thousands of labelled examples and supervised learning went, for many, from glory to shame: Some criticised deep learning as a whole and others proclaimed that the way forward had to be alternatives to supervised learning: predictive, unsupervised, semi-supervised and, more recently, self-supervised learning. However, all these seem brand names, rather than actual categories of a theoretically grounded taxonomy. Moreover, the call to banish supervised learning was motivated by the questionable claim that humans learn with little or no supervision and are capable of robust out-of-distribution generalisation. Here, we review insights about learning and supervision in nature, revisit the notion that learning and generalisation are not possible without supervision or inductive biases and argue that we will make better progress if we just call it by its name.
