विधियों की तुलना करें

	माम्बा (स्टेट स्पेस मॉडल)	स्थानिक-सामयिक ग्राफ़ कनवल्शनल नेटवर्क	स्विन् ट्रान्सफॉर्मर
क्षेत्र	गहन अधिगम	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2023	2018	2021
प्रवर्तक	Albert Gu	Sijie Yan	Ze Liu
प्रकार	Neural network architecture	Neural network architecture	Neural network architecture
मौलिक स्रोत	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).	Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022).
उपनाम	Mamba, State space models, Selective state space	ST-GCN, Spatial-Temporal Graph CNN	Swin, Hierarchical Vision Transformer
संबंधित	4	4	4
सारांश	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.	The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency.