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Explore the fundamental tradeoffs between State Space Models (SSMs) and Transformers in this 49-minute lecture by Albert Gu from Carnegie Mellon University. Gain a high-level overview of SSMs, a recently popular subquadratic alternative to Transformers in the field of computational modeling. Delve into the characteristics of these models and understand their implications for machine learning and artificial intelligence. Learn how SSMs offer potential advantages in terms of computational efficiency while considering their trade-offs compared to the widely-used Transformer architecture. Discover insights that can inform decisions about model selection and implementation in various AI applications.
