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

大脑具有从大量复杂和不断变化的感觉信息中快速、准确地提取含义的非凡能力。一个关键问题是神经元系统如何编码有关外部世界的信息，特别是关于物体识别和分类等知觉任务的信息。解开这个秘密可能会改变我们为计算机视觉而进行机器学习的典型方式--这是人工智能的一个关键领域。这个项目试图使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能力，这些能力可以过渡到商业和政府应用程序，在这些应用程序中，视觉信息的处理是一个令人担忧的问题。产生的识别模型可能会对包括计算机视觉、机器学习、神经科学、心理学和认知科学在内的多个领域产生巨大影响。此外，PI将培训本科生和研究生，并组织新的研讨会，将计算机科学与人类视觉联系起来。该项目的第一个技术目标是致力于研究一种潜在的变革性极值理论(EVT)，用于视觉识别。这一新的理论框架将为视觉科学家和人工智能工程师进行模拟人类视觉系统的实验提供坚实的基础。它将有助于在视觉环境中对决策进行新的理论分析，以及在操作上更具生物学一致性的新分类算法。第二个技术目标是对EVT识别模型进行实验评估。EVT的预测与更传统的依赖中心倾向假设的建模策略的显著差异，使我们能够制定可检验的假设，支持心理物理研究，以了解极端在识别中的作用。高层设计是在两个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理。第三个技术目标是使用开发的理论和实验结果来创建一类新的生物一致的机器学习算法，用于决策，这是一种超越最先进水平的可衡量的进步。这包括用于对特定类表示的分布建模的生成性学习算法，以及用于找到类之间边界的判别性学习算法。这项工作将开发概率生成性EVT混合模型，以及概率判别性一类、二类和多类EVT分类器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.