Deep SSM

Deep state-space models (SSMs) are a class of deep learning architectures designed for efficient and effective sequence modeling, offering advantages over traditional methods like transformers in terms of computational complexity and scalability for long sequences. Current research focuses on improving the scalability and performance of SSMs, particularly through architectures like Mamba and its variants, which employ selective scanning algorithms to efficiently model global context. These advancements are impacting various fields, including computer vision (image and video processing, object detection), 3D modeling, and reinforcement learning, by enabling more efficient and accurate processing of long temporal sequences and high-dimensional data.