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 2841–2850 of 3073 papers

TitleStatusHype
On Efficiently Acquiring Annotations for Multilingual ModelsCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
On Initial Pools for Deep Active LearningCode0
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron DiffractometryCode0
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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