Efficiently Modeling Long Sequences with Structured State Spaces - Albert Gu

Explore a groundbreaking approach to modeling long sequences in this Stanford University lecture by Albert Gu. Delve into the Structured State Space sequence model (S4), a novel technique designed to handle extensive dependencies in various data types. Learn how S4 combines state space models with HiPPO theory to efficiently process sequences exceeding 10,000 steps. Discover its applications across diverse benchmarks, particularly in continuous signal data like images, audio, and time series. Gain insights into the mathematical foundations and computational efficiency of S4, and understand its potential to revolutionize sequence modeling across multiple domains.

Introduction
Sequence Models
Types of Sequence Data
Temporal Data
Audio Data
Long Range Arena
Conceptual Idea
Visualization
Reconstruction
Defining S4
Correlation
Why are matrices needed
Why are matrices computationally difficult
Questions
Biosignal Data
Time Series Data
Rescaling
Conclusion