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

大脑具有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相关信息，特别是关于感知任务，如物体识别和分类。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-人工智能的一个关键领域。该项目旨在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功能，这些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问题。所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许多领域产生巨大影响。此外，PI还将对本科生和研究生进行培训，并组织新的计算机科学与人类视觉相关的研讨会。该项目的第一个技术目标是致力于视觉识别的潜在变革性极值理论（EVT）。这个新的理论框架将为视觉科学家和人工智能工程师提供坚实的基础，这些工程师正在进行模拟人类视觉系统的实验。它将促进视觉环境中决策的新理论分析，以及在操作中更具生物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和更传统的建模策略，依赖于集中趋势假设的预测的显着差异，使我们能够制定可检验的假设，支持心理物理学研究，以了解识别的极值的作用。高水平设计是两种不同制度下的三个实验，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。 第三个技术目标是使用开发的理论和实验结果来创建一类新的生物学一致的机器学习算法，用于决策，这是超越最先进技术的可衡量的进步。这包括生成学习算法来模拟特定类别表示的分布，以及判别学习算法来找到类别之间的边界。这项工作将开发概率生成EVT混合模型，以及概率判别单类，二进制和多类EVT分类器。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.