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On the Tradeoffs of State Space Models vs. Transformers

Offered By: Simons Institute via YouTube

Tags

Artificial Intelligence Courses Machine Learning Courses Neural Networks Courses Time Series Analysis Courses Computational Models Courses Transformers Courses Sequence Modeling Courses

Course Description

Overview

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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.

Syllabus

On the Tradeoffs of State Space Models


Taught by

Simons Institute

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