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 12711280 of 3073 papers

TitleStatusHype
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies0
A Lagrangian Duality Approach to Active Learning0
Deep Active Learning with Budget Annotation0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions0
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification0
Deep Active Learning with Manifold-preserving Trajectory Sampling0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion0
Deep Active Learning with Noisy Oracle in Object Detection0
Deep Active Learning with Structured Neural Depth Search0
Adaptive Active Learning for Image Classification0
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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