A new model called Mamba, which is a linear-time sequence modeling with selective state spaces, has been announced. It achieves state-of-the-art performance across various modalities such as language, audio, and genomics. Mamba outperforms Transformers in language modeling and enjoys fast inference due to clever improvements, challenging the dominant Transformer architecture.
Extremely Cool! Mamba, announced today, is a structured state space model that challenges the dominant Transformer architecture. Transformers are computationally inefficient, esp. when it comes to extended contexts. With some clever improvements, Mamba enjoys fast inference (5×… https://t.co/3veUFs0UGD https://t.co/Cf8dvZCjZh
[LG] Mamba: Linear-Time Sequence Modeling with Selective State Spaces https://t.co/6jOVEdOYI3 https://t.co/pvYaCteHCq
Mamba: Linear-Time Sequence Modeling with Selective State Spaces "As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers… https://t.co/e414WX8PoG
Mamba: Linear-Time Sequence Modeling with Selective State Spaces paper page: https://t.co/IIbOYoJRtR Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.… https://t.co/cAArkhTVgD https://t.co/nAxJHED8BM
[LG] Adaptive Training Distributions with Scalable Online Bilevel Optimization https://t.co/kmwfvwQ45f https://t.co/qOSwbhF4Pe