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 2821–2830 of 3073 papers

TitleStatusHype
Investigating Active Learning for Short-Answer Scoring—0
Towards ontology driven learning of visual concept detectors—0
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning—0
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems—0
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Active Learning for Community Detection in Stochastic Block Models—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