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 1091–1100 of 3073 papers

TitleStatusHype
Active Learning with Tabular Language Models—0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler—0
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—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