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

Showing 24912500 of 3073 papers

TitleStatusHype
RISAN: Robust Instance Specific Abstention NetworkCode0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
Correlation Clustering with Adaptive Similarity QueriesCode0
Risk-Aware Active Inverse Reinforcement LearningCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Partition-Based Active Learning for Graph Neural NetworksCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Cost Effective Active SearchCode0
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Benchmark Results

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