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

Showing 19811990 of 3073 papers

TitleStatusHype
Events Beyond ACE: Curated Training for Events0
Evidential uncertainties on rich labels for active learning0
Evolving Knowledge Distillation with Large Language Models and Active Learning0
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS0
Evolving Multi-Label Fuzzy Classifier0
Exact Sampling from Determinantal Point Processes0
Exemplar Guided Active Learning0
Experimental Design for Active Transductive Inference in Large Language Models0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning0
Experiments in Non-Coherent Post-editing0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified