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 1001–1010 of 3073 papers

TitleStatusHype
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Bayesian Hypernetworks—0
Active learning for energy-based antibody optimization and enhanced screening—0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling—0
Bayesian Active Learning for Sim-to-Real Robotic Perception—0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—0
Bayesian Generative Active Deep Learning—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—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