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 2121–2130 of 3073 papers

TitleStatusHype
Handling Adversarial Concept Drift in Streaming Data—0
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop—0
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification—0
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning—0
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community—0
Headnote Prediction Using Machine Learning—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition—0
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking—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