Few-Shot Learning
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Benchmarks
MedConceptsQA
DTD
FGVC Aircraft
Stanford Cars
PubMedQA
CR
Caltech101
CaseHOLD
EuroSAT
Flowers-102
GLUE QQP
MR
MRPC
MedNLI
OxfordPets
SUN397
StanforCars
UCF101
food101
tieredImageNet - 5-shot
Most implemented
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Language Models are Few-Shot Learners
LLaMA: Open and Efficient Foundation Language Models
Prototypical Networks for Few-shot Learning
Matching Networks for One Shot Learning
Papers
Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation
This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medical language, bridging a critical gap betw…
Reading ComprehensionText SimplificationFew-Shot LearningSample-Conditioned Representation Selection for Audio Few-Shot Learning
Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the …
Few-Shot LearningLearning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs
Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conformal prediction relies…
Few-Shot LearningDecoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling ma…
Few-Shot LearningMetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact, specialized forecasters are more desira…
Computational EfficiencyTime Series ForecastingFew-Shot LearningEvaluating and Explaining Prompt Sensitivity of LLMs Using Interactions
The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes in prompts can cause dramatic fluctuations in performance, a phenomen…
Few-Shot Learning