Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer.

We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection.

Infants possess a remarkable ability to rapidly learn and process visual inputs.

To address such an issue, we propose P ACE, marrying generalization of P Arameter-efficient fine-tuning with Consistency rEgularization.

However it often comes at a computational price which may hinder their deployment.