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 671–680 of 3073 papers

TitleStatusHype
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees—0
A Competitive Algorithm for Agnostic Active Learning—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Turn-Level Active Learning for Dialogue State TrackingCode0
Bayesian Active Learning in the Presence of Nuisance Parameters—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