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 110 of 3073 papers

TitleStatusHype
Active Learning for Neural PDE SolversCode5
On the reusability of samples in active learningCode5
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object DetectionCode4
Let's Verify Step by StepCode4
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point SupervisionCode3
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language ModelsCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
DCoM: Active Learning for All LearnersCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
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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