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 341–350 of 3073 papers

TitleStatusHype
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Generalized Coverage for More Robust Low-Budget Active Learning—0
The Future of Data Science EducationCode0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination—0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Automated Neural Patent Landscaping in the Small Data Regime—0
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations—0
Dataset Quantization with Active Learning based Adaptive SamplingCode1
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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