Abstract Demystifies how Mamba models efficiently handle extremely long sequences while outperforming Transformers in speed, scalability, and memory usage. We break down the core ideas behind selective state-space models, why they excel where attention struggles, and what this shift means for the future of sequence modeling.
Speaker Bio Short Bio: Younis is an (NLP Research Engineer ll) and a Computer Science Master’s student specializing in multimodal AI systems and advanced speech-modeling, with research spanning conversational analysis, interaction modeling, and real-time systems.