Multi-Bounce Attention Explained in 3 Minutes! | Understanding Information Flow in Transformers скачать в хорошем качестве

Multi-Bounce Attention Explained in 3 Minutes! | Understanding Information Flow in Transformers

🧠 What if transformer attention is not just a matrix… but a dynamical system? Attention is the core mechanism behind modern transformers, yet most analyses only look at direct token interactions. This video explores a powerful new interpretation where attention matrices are viewed as discrete-time Markov chains, revealing how information actually flows across tokens over multiple steps. Instead of analyzing attention statically, this perspective models attention as a probabilistic transition process. By propagating attention through multiple transitions, we uncover higher-order relationships, global token importance, and a steady-state representation called TokenRank. In this video, we cover: ✅ Why attention matrices behave like stochastic transition systems ✅ Multi-bounce attention and higher-order token interactions ✅ TokenRank and global token importance ✅ Why eigenvalues reveal meaningful attention heads ✅ How this improves segmentation, visualization, and diffusion models This interpretation provides a deeper theoretical understanding of transformers and offers practical tools for explainability and downstream improvements. #machinelearning #deeplearning #Transformers #attentionmechanism #visiontransformers #explainableai #airesearch #neuralnetworks #representationlearning #computervision #aitheory #3MinutePaper