방법 비교

	맘바 (상태 공간 모델)	공간-시간 그래프 컨볼루션 네트워크
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2023	2018
창시자	Albert Gu	Sijie Yan
유형	Neural network architecture	Neural network architecture
원전	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32).
별칭	Mamba, State space models, Selective state space	ST-GCN, Spatial-Temporal Graph CNN
관련	4	4
요약	Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.	Spatial-Temporal Graph Convolutional Networks (ST-GCN) is an architecture introduced by Yan et al. in 2018 for skeleton-based action recognition. By modeling human skeletons as graphs where joints are nodes and bones are edges, ST-GCN applies graph convolutions across space and time to recognize actions from skeleton sequences.