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Mamba’s Performance in DNA, Audio, and Speed Benchmarks


Mamba’s Performance in DNA, Audio, and Speed Benchmarks by @rendering

Mamba proves its strength in long-range dependencies, outperforming HyenaDNA in DNA sequence modeling and surpassing state-of-the-art speech generation models. Selective SSMs also demonstrate superior efficiency in AI training benchmarks.

Table of Links

Abstract and 1. Introduction

2 State Space Models

3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression

3.2 Improving SSMs with Selection

3.3 Efficient Implementation of Selective SSMs

3.4 A Simplifed SSM Architecture

3.5 Properties of Selection Mechanisms

3.6 Additional Model Details

4 Empirical Evaluation and 4.1 Synthetic Tasks

4.2 Language Modeling

4.3 DNA Modeling

4.4 Audio Modeling and Generation

4.5 Speed and Memory Benchmarks

4.6 Model Ablations

5 Discussion

6 Conclusion, Acknowledgments and References

A Discussion: Selection Mechanism

B Related Work and B.1 S4 Variants and Derivatives

B ...


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