Open Source
Open-source initiatives are democratizing access to and accelerating advancements in various scientific and technological fields by fostering collaboration and reproducibility. Current research focuses on developing open-source tools and datasets across diverse domains, including AI models (like LLMs and specialized models for tasks such as medical diagnosis or autonomous driving), simulation environments for robotics and network analysis, and benchmarks for evaluating model performance. This open approach significantly benefits the scientific community by enabling wider participation, facilitating rigorous validation, and accelerating the translation of research into practical applications across numerous sectors.
Papers
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models
Fan Fan, Georgia Martinez, Thomas Desilvio, John Shin, Yijiang Chen, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk
Mini-Giants: "Small" Language Models and Open Source Win-Win
Zhengping Zhou, Lezhi Li, Xinxi Chen, Andy Li