raminmh/liquid-s4
Liquid Structural State-Space Models
This project helps machine learning practitioners build models that analyze sequential data more effectively. It takes various forms of time-series data, such as medical signals, audio recordings, or text, and produces advanced models capable of tasks like heart rate estimation or speech recognition. It is ideal for researchers and engineers working with dynamic data streams who need efficient and accurate long-range sequence modeling.
388 stars. No commits in the last 6 months.
Use this if you need to build high-performing models for tasks involving continuous, time-dependent data like physiological signals, audio, or lengthy text sequences.
Not ideal if your primary focus is on static image classification or other non-sequential data problems.
Stars
388
Forks
67
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 01, 2024
Commits (30d)
0
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