Võrdle meetodeid

	Mamba (oleku-ruumi mudel)	Maskeeritud autoenkoodrid	QLoRA
Valdkond	Süvaõpe	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2023	2021	2023
Looja	Albert Gu	Kaiming He	Tim Dettmers
Tüüp	Neural network architecture	Neural network architecture	Training methodology
Algallikas	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009).	Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314.
Rööpnimetused	Mamba, State space models, Selective state space	MAE, Vision MAE	QLoRA, Quantized LoRA
Seotud	4	4	4
Kokkuvõte	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.	Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels.	QLoRA is an efficient fine-tuning method introduced by Dettmers et al. in 2023 that enables fine-tuning large language models using quantization and low-rank adaptation. By combining 4-bit quantization with LoRA, QLoRA reduces memory requirements by 75%, enabling fine-tuning of 65B-parameter models on single GPUs.