Efficient State Space Models
Albert discusses the evolution of state space models, emphasizing the introduction of learnable parameters that remain static across sequences. He shares insights on simplifying model structures while maintaining performance and the importance of making transition parameters data dependent. Collaborating with Tree, they developed custom GPU kernels to enhance efficiency, showcasing a blend of hardware expertise and innovative model design.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Mamba, Mamba-2 and Post-Transformer Architectures for Generative AI with Albert Gu - 693
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