Designing computationally efficient network architectures remains an ongoing necessity in
computer vision. In this paper, we adapt Mamba, a state-space language model, into …

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 …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

Models using structured state space sequence (S4) layers have achieved state-of-the-art
performance on long-range sequence modeling tasks. An S4 layer combines linear state …

Transformers have surpassed RNNs in popularity due to their superior abilities in parallel
training and long-term dependency modeling. Recently, there has been a renewed interest …

Mamba, a recent selective structured state space model, performs excellently on long
sequence modeling tasks. Mamba mitigates the modeling constraints of convolutional …

Abstract Machine learning models are increasingly being scaled in both sequence length
and model dimension to reach longer contexts and better performance. However, existing …

Sequence modeling is a crucial area across various domains, including Natural Language
Processing (NLP), speech recognition, time series forecasting, music generation, and …

State space models (SSMs) have high performance on long sequence modeling but require
sophisticated initialization techniques and specialized implementations for high quality and …

Transformers with linear attention allow for efficient parallel training but can simultaneously
be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear (with …