Learn how new techniques make AI models faster, smarter, and more efficient by reducing memory use and speeding up training.

Learn how incorporating input-dependent parameters transforms state space models from time-invariant to time-varying.

Discover how SSMs improve sequence modeling by leveraging context compression, selectivity, and hardware-aware algorithms for efficient AI performance.

Explore state space models (SSMs), their structured architecture, and innovations like H3, Hyena, and RWKV that revolutionize AI sequence modeling efficiency.

Mamba, a new linear-time model, matches Transformer performance with 5× higher efficiency, excelling in language, audio, and genomics tasks.

Explore the simplified state space model (SSM) architecture that combines linear attention and MLP components into a unified block.

Mamba matches GPT-3 in language modeling, outperforming leading models without using traditional attention mechanisms, setting a new standard for efficiency.

Mamba’s memory system excels in solving sequence tasks, outperforming other models with its ability to remember key data and handle longer sequences.

New research shows real-valued SSMs outperform complex ones in discrete tasks. We explore initialization strategies and their impact on SSM efficiency.

Selection mechanisms are a powerful tool in sequence modeling, improving models like RNNs, CNNs, and SSMs.