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on
1
Introduction
2
Sequence Models
3
Types of Sequence Data
4
Temporal Data
5
Audio Data
6
Long Range Arena
7
Conceptual Idea
8
Visualization
9
Reconstruction
10
Defining S4
11
Correlation
12
Why are matrices needed
13
Why are matrices computationally difficult
14
Questions
15
Biosignal Data
16
Time Series Data
17
Rescaling
18
Conclusion
Description:
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.

Efficiently Modeling Long Sequences with Structured State Spaces - Albert Gu

Stanford University
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