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 2171–2180 of 3073 papers

TitleStatusHype
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff—0
ALPINE: Active Link Prediction using Network Embedding—0
Boosting API Recommendation with Implicit Feedback—0
Active Learning for Identification of Linear Dynamical Systems—0
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm—0
Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys—0
A Graph-Based Approach for Active Learning in Regression—0
Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning—0
QActor: On-line Active Learning for Noisy Labeled Stream Data—0
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
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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