arxiv preprint – Mamba: Linear-Time Sequence Modeling with Selective State Spaces

In this episode we discuss Mamba: Linear-Time Sequence Modeling with Selective State Spaces
by Albert Gu, Tri Dao. The paper presents Mamba, an innovative neural network architecture that outperforms traditional Transformer models, especially in handling very long sequences. Mamba’s design incorporates selective structured state space models (SSMs) whose parameters depend on input tokens, enabling content-based reasoning and memory management over sequence lengths. The result is a model with fast inference, linear scaling with sequence length, and state-of-the-art performance in various modalities, including language, audio, and genomics, even surpassing Transformers that are twice its size.