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 9911000 of 3073 papers

TitleStatusHype
Bayesian optimization for robust robotic grasping using a sensorized compliant hand0
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
Boundary Matters: A Bi-Level Active Finetuning Framework0
Bayesian Hypernetworks0
Bounds on the Generalization Error in Active Learning0
Active learning for energy-based antibody optimization and enhanced screening0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings0
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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