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Found 3 papers, 1 papers with code
Revisiting Local Descriptor for Improved Few-Shot Classification
Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images.
Memory-Augmented Relation Network for Few-Shot Learning
Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.
Deep Recurrent Neural Network for Protein Function Prediction from Sequence
Some RNN predictions for the ferritin-like iron sequestering function were experimentally validated, even though their sequences differ significantly from known, characterized proteins and from each other and cannot be easily predicted using popular bioinformatics methods.
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