CAREER: Learning at the Edge: an Extreme Value Theory for Visual Recognition

大脑有一种非凡的能力，可以快速准确地从大量复杂且不断变化的感官信息中提取意义。一个关键的问题是神经元系统如何对外部世界的相关信息进行编码，特别是在对象识别和分类等感知任务方面。解开这个秘密可能会改变我们处理计算机视觉机器学习的典型方式——这是人工智能的一个关键领域。该项目试图利用心理学和统计学的方法和程序来回答这个问题，从而产生新的人工智能功能，可以过渡到商业和政府应用中，其中视觉信息的处理是一个关注的问题。所产生的识别模型可能会对许多领域产生巨大影响，包括计算机视觉、机器学习、神经科学、心理学和认知科学。此外，PI将在本科和研究生阶段培训学生，并组织有关计算机科学与人类视觉的新研讨会。该项目的第一个技术目标是为视觉识别提供潜在的变革性极值理论（EVT）。这一新的理论框架将为从事人类视觉系统建模实验的视觉科学家和人工智能工程师提供坚实的基础。它将促进视觉环境下决策的新理论分析，以及在操作中更具有生物学一致性的新分类算法。第二个技术目标是EVT识别模型的实验评估。EVT和依赖集中趋势假设的传统建模策略在预测上的显著差异，使我们能够制定可测试的假设，支持心理物理学研究，以理解极端值在识别中的作用。高层次的设计是在两种不同的制度下进行的三个实验，以人类为研究重点。这项研究旨在揭示支撑物体识别的决策原则。第三个技术目标是利用已开发的理论和实验结果来创建一类新的生物一致性机器学习算法，用于决策，这是一个超越最先进技术的可衡量的进步。这包括生成式学习算法，用于对特定类表示的分布进行建模，以及判别式学习算法，用于找到类之间的边界。这项工作将开发概率生成EVT混合模型，以及概率判别单类、二元和多类EVT分类器。该奖项反映了美国国家科学基金会的法定使命，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。

The brain has the remarkable ability to rapidly and accurately extract meaning from a flood of complex and ever-changing sensory information. A key question is how neuronal systems encode relevant information about the external world, especially with respect to perceptual tasks such as object recognition and categorization. Unlocking this secret would likely change the typical way in which we approach machine learning for computer vision --- a key area of artificial intelligence. This project seeks to answer this question using methods and procedures from psychology and statistics, leading to new AI capabilities that can be transitioned to commercial and government applications where the processing of visual information is a concern. The recognition model produced could have tremendous impact across a number of fields including computer vision, machine learning, neuroscience, psychology, and cognitive science. In addition, the PI will train students at the undergraduate and graduate levels, and organize new workshops relating computer science to human vision.The project’s first technical objective is to work towards a potentially transformative Extreme Value Theory (EVT) for visual recognition. This new theoretical framework will provide solid grounding for vision scientists and AI engineers working on experiments that model the human visual system. It will facilitate both new theoretical analyses of decision making in a visual context, as well as new classification algorithms that are more biologically-consistent in operation. The second technical objective is an experimental assessment of the EVT recognition model. The significant difference in predictions made by EVT and more conventional modeling strategies that rely on central tendency assumptions allows us to formulate testable hypotheses that support psychophysical studies to understand the role of extrema in recognition. The high-level design is three experiments in two different regimes, with humans as the focus of study. This study aims to uncover the principles of decision making that underpin object recognition. The third technical objective is to use the developed theory and the experimental results to create a new class of biologically-consistent machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes generative learning algorithms to model the distributions for specific class representations, and discriminative learning algorithms to find the boundaries between classes. This effort will develop probabilistic generative EVT mixture models, as well as probabilistic discriminative one-class, binary, and multi-class EVT classifiers.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.