训练集,在AI领域多指用于机器学习训练的数据,数据可以有标签的,也可以是无标签的。

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题目: ImageNet Classification with Deep Convolutional Neural Networks

摘要:

我们训练了一个大型的深度卷积神经网络,将LSVRC-2010 ImageNet训练集中的130万幅高分辨率图像分成1000个不同的类。在测试数据上,我们获得了前1名和前5名的错误率,分别为39.7%和18.9%,这比之前的最新结果要好得多。该神经网络有6000万个参数和50万个神经元,由5个卷积层组成,其中一些是最大池化层,还有两个全局连接层,最后是1000路的softmax。为了加快训练速度,我们使用了不饱和的神经元和一个非常高效的卷积网络GPU实现。为了减少全局连通层中的过拟合,我们采用了一种新的正则化方法,该方法被证明是非常有效的。

作者:

Ilya Sutskever是OpenAI的联合创始人和首席科学家,之前是斯坦福大学博士后,研究领域是机器学习,神经网络。

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Many applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually complicated mathematical functions that map from an input space to an output space. Stakeholders are interested to know the rationales behind models' decisions and functional behavior. We study this functional behavior in relation to the data used to create the models. On this topic, scholars have often assumed that models do not extrapolate, i.e., they learn from their training samples and process new input by interpolation. This assumption is questionable: we show that models extrapolate frequently; the extent of extrapolation varies and can be socially consequential. We demonstrate that extrapolation happens for a substantial portion of datasets more than one would consider reasonable. How can we trust models if we do not know whether they are extrapolating? Given a model trained to recommend clinical procedures for patients, can we trust the recommendation when the model considers a patient older or younger than all the samples in the training set? If the training set is mostly Whites, to what extent can we trust its recommendations about Black and Hispanic patients? Which dimension (race, gender, or age) does extrapolation happen? Even if a model is trained on people of all races, it still may extrapolate in significant ways related to race. The leading question is, to what extent can we trust AI models when they process inputs that fall outside their training set? This paper investigates several social applications of AI, showing how models extrapolate without notice. We also look at different sub-spaces of extrapolation for specific individuals subject to AI models and report how these extrapolations can be interpreted, not mathematically, but from a humanistic point of view.

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