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 981990 of 3073 papers

TitleStatusHype
Bayesian optimization for robust robotic grasping using a sensorized compliant hand0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora0
Bilingual Transfer Learning for Online Product Classification0
Bayesian Nonparametric Crowdsourcing0
Actively learning to learn causal relationships0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences0
Bayesian Hypernetworks0
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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