State Space Model
State space models (SSMs) are a powerful class of models used to represent dynamic systems by tracking their hidden states over time. Current research focuses on developing efficient SSM architectures, such as Mamba and its variants, to overcome limitations of traditional methods in handling long sequences and high-dimensional data, particularly in applications involving time series forecasting, image processing, and dynamic system modeling. These advancements are improving the accuracy and scalability of SSMs across diverse fields, leading to significant improvements in areas like medical image analysis, autonomous driving, and natural language processing. The resulting models offer a compelling alternative to computationally expensive methods like transformers, while maintaining or exceeding performance in many applications.
Papers - Page 3
Enhancing Memory and Imagination Consistency in Diffusion-based World Models via Linear-Time Sequence Modeling
Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun, Chun-Yi LeeSatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models
Chuc Man Duc, Hiromichi FukuiSub-Sequential Physics-Informed Learning with State Space Model
Chenhui Xu, Dancheng Liu, Yuting Hu, Jiajie Li, Ruiyang Qin, Qingxiao Zheng, Jinjun Xiong
A Separable Self-attention Inspired by the State Space Model for Computer Vision
Juntao Zhang, Shaogeng Liu, Kun Bian, You Zhou, Pei Zhang, Jianning Liu, Jun Zhou, Bingyan LiuSSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG
Cunhang Fan, Sheng Zhang, Jingjing Zhang, Zexu Pan, Zhao Lv