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 1561–1570 of 3073 papers

TitleStatusHype
Active Learning: Sampling in the Least Probable Disagreement Region—0
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Sample Complexity of Deep Active Learning—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Best Practices in Pool-based Active Learning for Image Classification—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?—0
Pretrained models are active learners—0
SABAL: Sparse Approximation-based Batch Active Learning—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