Speaker
Ahmed Hammad
(KEK, Japan)
Description
The Transformer network, originally introduced for online language translation, has recently achieved remarkable success, particularly with the emergence of the GPT family. In this talk, I will explore how Transformer architectures can be adapted for analyses in particle colliders. At the core of Transformer models lies the attention mechanism. I will review various types of attention mechanisms, including self-attention, cross-attention, and sparse attention. Finally, I will discuss how different AI interpretability techniques can be applied to gain insight into the model decision-making process, effectively transforming machine learning from a "black box" into a more transparent "white box" system.
Author
Ahmed Hammad
(KEK, Japan)
