in-context-learning

This paper proposes ACSESS, a method for automatically combining multiple sample selection strategies to improve few-shot learning across both in-context learning and gradient-based approaches. The work demonstrates that combining strategies consistently outperforms individual selection methods across 14 datasets with both text and image modalities.

This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.