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

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Showing 211–220 of 3073 papers

TitleStatusHype
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Stream-Based Active Learning for Process Monitoring—0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
Targeting Negative Flips in Active Learning using Validation SetsCode0
Learning Quantitative Automata Modulo Theories—0
Deep Active Learning in the Open World—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
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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