विधियों की तुलना करें

	स्थानिक-सामयिक ग्राफ़ कनवल्शनल नेटवर्क	माम्बा (स्टेट स्पेस मॉडल)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2018	2023
प्रवर्तक	Sijie Yan	Albert Gu
प्रकार	Neural network architecture	Neural network architecture
मौलिक स्रोत	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).	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.
उपनाम	ST-GCN, Spatial-Temporal Graph CNN	Mamba, State space models, Selective state space
संबंधित	4	4
सारांश	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.	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.