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This project investigates few-shot learning for relation extraction using the FewRel dataset. We will compare Prototypical Networks, MAML, and k-NNs in different few-shot settings to see which performs best with minimal data. The goal is to improve relation extraction in NLP by effectively handling data scarcity.
The official repo for the extension of [NeurIPS'22] "APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking": https://github.com/pandorgan/APT-36K
[NeurIPS 2023] The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification