Comparar métodos

	Mamba (Modelo de Espaço de Estados)	TimeGPT	Vision Mamba
Área	Aprendizado profundo	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2023	2023	2024
Autor original	Albert Gu	Fabio Garza	Li Zhu
Tipo	Neural network architecture	Neural network architecture	Neural network architecture
Fonte seminal	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	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.
Outros nomes	Mamba, State space models, Selective state space	TimeGPT-1, Time series GPT	ViM, Mamba for Vision
Relacionados	4	4	4
Resumo	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.	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.