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 3140 of 3073 papers

TitleStatusHype
InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional MaterialsCode1
Efficient Process Reward Model Training via Active LearningCode1
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 BasketballCode1
Post-hoc Probabilistic Vision-Language ModelsCode1
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement ParadigmCode1
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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