EAGER: Creating an Unsupervised Interpretable Representation of the World Through Concept Disentanglement

人类能够将一个大的实体分解成更小更简单的概念，仅仅是因为看到了许多对象及其关系。在机器学习模型中再现这种类型的行为有几个好处。特别是，它可以导致计算方式来表示世界，这是可解释的，但功能强大。这些新的表示可以在机器学习算法中使用，从而使算法在底层情况发生变化时更加鲁棒，更有可能泛化。例如，如果一个算法找到了一个对象通常由哪些部分组成的集合，那么它可以使用这些部分来识别这种类型的对象，即使它处于不寻常的设置中，或者当对象本身不寻常时。这种表示世界的新方式将允许更强大和可推广的机器学习模型。这将特别有助于解决计算机视觉领域的难题，包括与自动驾驶车辆中的视觉系统相关的问题，医疗时间序列分析，以及与理解材料特性和发现新材料相关的材料科学问题。具体而言，该项目的主要目标是使用解纠缠神经网络学习可解释的学习概念。该方法将问题分解为三个步骤，每个步骤都可以管理，每个步骤都可以独立于其他步骤进行检查和改进。这些步骤是将每个观察分解为局部部分，通过查看局部部分之间的共同关系来识别可能的概念，并根据其语义含义在一个分离的神经网络中对齐所提出的概念。发现的概念将是可解释的，并且可以用作许多下游任务的特征。与其他方法相比，从这些概念中构建的神经网络可能更容易推广到新的情况。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Humans are able to break down a large entity into smaller and simpler concepts, just from having seen many objects and their relationships. Reproducing this type of behavior in a machine learning model has several benefits. In particular, it could lead to computational ways of representing the world that are interpretable yet powerful. These new representations could be used within machine learning algorithms, allowing the algorithms to be more robust and more likely to generalize when the underlying situations change. For instance, if an algorithm has found a collection of parts that an object is typically comprised of, then it can use those parts to identify this type of object even when it is in an unusual setting, or when the object itself is unusual. This new way of representing the world will allow more robust and generalizable machine learning models. This will be particularly helpful for difficult challenges in computer vision, including problems related to vision systems in automated vehicles, analysis of medical time-series, and materials science problems related to the understanding of material properties and discovery of new materials.Specifically, the main goal of this project is learning with interpretable learned concepts using a disentangled neural network. The approach breaks the problem down into three steps that each could be manageable, and each step can be checked and improved independently of the other steps. The steps are to decompose each observation into local parts, identify possible concepts by looking at common relationships between the local parts, and align the proposed concepts, based on their semantic meaning, within a disentangled neural network. The discovered concepts will be interpretable and can be used as features for many downstream tasks. The disentangled neural networks built from these concepts could potentially generalize more easily to new situations than other approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.