February 26, 2024Open Access

Training Neural Networks from Scratch with Parallel Low-Rank Adapters

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Model pre-training can be enhanced with low-rank adaptation techniques, particularly low-rank heads.
The bi-level optimization algorithm, LoRA-the-Explorer, enables efficient parallel training across computing nodes.
Experiments with vision transformers indicate that LTE competes well against standard pre-training methods with reduced synchronization needs. The findings reveal a promising avenue for pre-training deep learning models efficiently.

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Explores low-rank adaptation for model pre-training, highlighting efficiency in computing resources.

Huh et al. (2024) studied this question.

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