Yielding and Exploiting Confidence in Deep Learning

深度学习的研究和应用规模持续加速。在我们的工作和日常生活中，我们越来越依赖这些系统来进行预测。在某些情况下，它们是自动化的，而在其他情况下，它们涉及人类“在循环中”。不幸的是，这些系统可能悄无声息地失败。失败的例子包括错误但高度自信的预测，以及容易受到对抗性攻击和所谓的愚弄图像。构建更具容错性的机器学习系统的一种方法是校准它们的置信度或不确定性措施，以供人类或其他系统解释。这些措施是有用的，例如，将数据点提交给人类专家或另一个系统进行进一步处理。提出估算置信度或不确定性的不同技术是一个活跃的研究领域。大多数方法的目的是在训练后估计或重新校准置信度度量；然而，自信在学习过程中有很多用途。示例包括为注释选择额外的数据点（主动学习），选择接下来访问哪些示例（自定节奏学习），以及使用拒绝选项进行学习。我提出了一个研究计划，将专注于在训练和部署机器学习系统中表达、量化和引导信心的新颖而有效的方法。长期目标是让系统在自主方面承担更多责任，并改善与人类的协作。它将跨越三个活动。我们将比较和开发不同的置信度度量：1)使系统能够最优地从oracle请求“提示”；2)调制应用于输入的数据增强的类型和数量；3)辅助任务的选择。我们的目标是用更少的人工标记的例子来更快地学习。我们将研究动态网络架构，其处理依赖于通过置信度估计的输入。例如，在训练期间，一个简单的例子可能需要很少的计算，一个中等的例子可能需要更多的计算，而一个困难的例子可以被忽略，并在未来的一个时期重新审视。我们的目标是在训练和部署期间优化计算。评估和跨学科协作在报告性能时，典型的机器学习基准不会考虑某些任务比其他任务成本更高。此外，置信度估计很少直接评估：它们使用像分布外或对抗性样本检测这样的代理。我们将与神经科学家合作，评估量化人类决策中的信心的方法，并更好地理解它在人类学习中的作用。我们将为在机器学习中使用置信度制定一个标准基准。该研究项目将开发更强大、更高效、更透明的深度学习模型，旨在改善生活。

The scale of research and application of deep learning continues to accelerate. In our work and our daily lives, we see more dependence on these systems for making predictions. In some cases, they are automated, and in others, they involve humans "in-the-loop". Unfortunately, these systems can fail silently. Examples of failure include erroneous yet highly confident predictions, as well as susceptibility to adversarial attacks and so-called fooling images. One way to build more fault-tolerant machine learning systems is to calibrate their confidence or uncertainty measures for interpretation by humans or other systems. Such measures are useful, for example, to refer data points to a human expert or another system for further processing. Proposing different techniques for estimating confidence or uncertainty is an active area of research. Most approaches aim to either estimate or re-calibrate a confidence measure after training; however, there are many uses for confidence throughout learning. Examples include choosing additional data points for annotation (active learning), selecting which examples to visit next (self-paced learning), and learning with a reject option. I propose a research program that will focus on novel and efficient ways of expressing, quantifying, and channeling confidence, both in training and deploying machine learning systems. The long-term goal is toward systems with more responsibility in their autonomy and improved collaboration with humans. It will span three activities. Modulating the learning experience We will compare and develop different confidence measures to: 1) enable a system to optimally ask for "hints" from an oracle; 2) modulate the type and amount of data augmentation applied to an input; and 3) choose among auxiliary tasks. Our goal is to learn faster with less human-labeled examples. Confidence and conditional computation We will investigate dynamic network architectures whose processing depends on the input through confidence estimates. For example, during training, a simple example could require very little computation, a moderate example could require more computation, and a difficult example could be ignored and revisited at a future epoch. Our goal is to optimize computation during training and deployment. Evaluation and cross-discipline collaboration In reporting performance, typical machine learning benchmarks do not consider that some tasks are more costly than others. Moreover, confidence estimates are rarely evaluated directly: they use proxies like out-of-distribution or adversarial example detection. We will work with neuroscientists to assess methods for quantifying confidence in human decision making and gain a better understanding of its role in human learning. We will develop a standard benchmark for the use of confidence in machine learning. This research program will develop more robust, efficient, and transparent deep learning models designed to improve life.