Conveners
Session9
- Hironao Miyatake
The use of machine learning (ML) in high energy physics has exploded in the past decade. While it has provide impressive improvements across a broad range of use cases, it has typically been limited to uses with data already collected by experiments. I will discuss the challenges involved with the use of ML on FPGAs in trigger and data acquisition systems in general as well as specific...
There has been a significant progress in development of large language models in the industries. These models use self-supervised learning methods through which a big AI model can learn how to effectively capture a greater scope of contexts and result in so-called Foundation Models (FMs). Thanks to their strong encoding capability that extracts a comprehensive set of key features in data, once...
Machine learning and physics have long been deeply intertwined, and there have been eras when their relationship came to the forefront. Even in today’s revolutionary AI development, physics has played a significant role—for example, in diffusion models. From a physics standpoint as well, an integrative perspective across various specialized domains is provided by innovative new mathematical...
