SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 3073 papers

TitleStatusHype
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
An Information Retrieval Approach to Building Datasets for Hate Speech DetectionCode0
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
Adapting Coreference Resolution Models through Active LearningCode0
Active Labeling: Streaming Stochastic GradientsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Bayesian Dark KnowledgeCode0
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelCode0
Identifying Adversarially Attackable and Robust SamplesCode0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in RobotsCode0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Improving traffic sign recognition by active searchCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified