SOTAVerified

Few-Shot Learning

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

Papers

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Showing 951–1000 of 2964 papers

TitleStatusHype
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Selecting Between BERT and GPT for Text Classification in Political Science Research—0
Quantum Diffusion Models for Few-Shot Learning—0
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning—0
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning—0
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs—0
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Large Language Models for Patient Comments Multi-Label Classification—0
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient—0
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation—0
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Vulnerability of LLMs to Vertically Aligned Text Manipulations—0
Prompt Diffusion Robustifies Any-Modality Prompt Learning—0
Integrating Large Language Models with Internet of Things Applications—0
Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts—0
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Composing Diffusion Policies for Few-shot Learning of Movement Trajectories—0
Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological imagesCode0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
EPIC: Efficient Position-Independent Caching for Serving Large Language Models—0
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions—0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for ClassificationCode0
KNN Transformer with Pyramid Prompts for Few-Shot Learning—0
Neural networks that overcome classic challenges through practice—0
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework—0
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition—0
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions—0
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images—0
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity—0
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?—0
DemoShapley: Valuation of Demonstrations for In-Context Learning—0
Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial LiteracyCode0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
DCP: Learning Accelerator Dataflow for Neural Network via Propagation—0
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis—0
Manual Verbalizer Enrichment for Few-Shot Text Classification—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning—0
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Revisiting In-context Learning Inference Circuit in Large Language ModelsCode0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified