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 181–190 of 3073 papers

TitleStatusHype
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?—0
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty—0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models—0
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data—0
Label Distribution Learning using the Squared Neural Family on the Probability Simplex—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Class Balance Matters to Active Class-Incremental LearningCode0
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 BasketballCode1
Post-hoc Probabilistic Vision-Language ModelsCode1
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for ElectrocatalysisCode0
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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