kyegomez/HSSS
Implementation of a Hierarchical Mamba as described in the paper: "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling"
This project offers an implementation of a Hierarchical Mamba architecture, which is a type of neural network. It takes a single sequence of input data, processes it through multiple levels of state space models, and produces multiple output sequences. This is designed for machine learning researchers and engineers who are experimenting with advanced sequence-to-sequence modeling techniques.
No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning researcher or engineer exploring novel neural network architectures for sequence modeling, particularly interested in hierarchical state space models like Mamba.
Not ideal if you need a plug-and-play solution for common sequence tasks without diving into the specifics of model architecture and training.
Stars
15
Forks
2
Language
Python
License
MIT
Category
Last pushed
Nov 11, 2024
Commits (30d)
0
Dependencies
4
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