CIS

ABSTRACT: As the sizes of Large Language Models (LLMs) continue to grow exponentially, it becomes imperative to explore novel computing paradigms that can address the dual challenge of scaling these models while adhering to constraints posed by compute and data resources. This presentation will delve into several strategies aimed at alleviating this dilemma: (1) refraining […]

ABSTRACT: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports to construct joint multimodal embedding that serves as a basis for classification. We demonstrate the advantages of this approach in application to assessment of pulmonary […]

ABSTRACT: Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people’s lives. My research focuses on developing interpretable algorithms and pipelines to ensure the safe and efficient utilization of machine learning models in the decision-making process. In this talk, I will introduce a new paradigm, called learning the Rashomon set, which finds and stores […]