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 2851–2875 of 3073 papers

TitleStatusHype
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in RobotsCode0
Self-Regulated Interactive Sequence-to-Sequence LearningCode0
Quantifying Local Model Validity using Active LearningCode0
Self-supervised 360^ Room Layout EstimationCode0
Active Learning for Regression Using Greedy SamplingCode0
Active learning for reducing labeling effort in text classification tasksCode0
Bayesian Active Learning By Distribution DisagreementCode0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry SystemCode0
Learning how to Active Learn: A Deep Reinforcement Learning ApproachCode0
Learning How to Active Learn by DreamingCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Optimal Data Selection: An Online Distributed ViewCode0
Self-supervised optimization of random material microstructures in the small-data regimeCode0
Learning Linear Dynamical Systems with Semi-Parametric Least SquaresCode0
Active DOP: A constituency treebank annotation tool with online learningCode0
Transferable Candidate Proposal with Bounded UncertaintyCode0
Explainable Active Learning for Preference ElicitationCode0
Online Adaptive Asymmetric Active Learning with Limited BudgetsCode0
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
Learning Objective-Specific Active Learning Strategies with Attentive Neural ProcessesCode0
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learningCode0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Exploiting Counter-Examples for Active Learning with Partial labelsCode0
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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