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Few-Shot Learning

27개 벤치마크 · 논문 3,290편 · 이 태스크의 논문 보기 →

Benchmarks

MedConceptsQA

결과 24개

DTD

결과 8개

FGVC Aircraft

결과 8개

Stanford Cars

결과 6개

PubMedQA

결과 4개

CR

결과 2개

Caltech101

결과 2개

CaseHOLD

결과 2개

EuroSAT

결과 2개

Flowers-102

결과 2개

GLUE QQP

결과 2개

MR

결과 2개

MRPC

결과 2개

MedNLI

결과 2개

OxfordPets

결과 2개

SUN397

결과 2개

StanforCars

결과 2개

UCF101

결과 2개

food101

결과 2개

Most implemented

Language Models are Few-Shot Learners

2020-05-28 · 구현 67개

Prototypical Networks for Few-shot Learning

2017-03-15 · 구현 43개

Matching Networks for One Shot Learning

2016-06-13 · 구현 26개

Papers

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

2026-09-09 · Xuan Cuong Ngo, Ngan Le arxiv

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 Learning

Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction

2026-08-27 · Mayanka Chandrashekar, Xi Zhang, Ethan Seefried, Tirthankar Ghosal 외 arxiv

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 Learning

MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters

2026-08-24 · ChengAo Shen, Wenchao Yu, Fangyu Wu, Dongjin Song 외 arxiv

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 Learning

Evaluating and Explaining Prompt Sensitivity of LLMs Using Interactions

2026-08-19 · Ruiyang Qin, Qingzhuo Wang, Tian Wang, Zhihua Wei 외 arxiv

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

Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach

2026-08-18 · Lu Xu, Xu Li, Linjiang Zheng, Fan Li 외 arxiv

Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explain…

Feature EngineeringFeature ImportanceFew-Shot Learning

Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks

2026-08-18 · Matin Amoozadeh, Amin Alipour arxiv

Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend not to ask enough questions in the classroom. However, studies have shown that st…

Few-Shot Learning

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