Comparar métodos

	Redes neuronales convolucionales espacio-temporales de grafos	TimeGPT	Visión Mamba
Campo	Aprendizaje profundo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2018	2023	2024
Autor original	Sijie Yan	Fabio Garza	Li Zhu
Tipo	Neural network architecture	Neural network architecture	Neural network architecture
Fuente seminal	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).	Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop.	Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning.
Alias	ST-GCN, Spatial-Temporal Graph CNN	TimeGPT-1, Time series GPT	ViM, Mamba for Vision
Relacionados	4	4	4
Resumen	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.	TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning.	Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.