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 17311740 of 3073 papers

TitleStatusHype
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection0
Work Smart - Reducing Effort in Short-Answer Grading0
Worst-Case Adaptive Submodular Cover0
Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge0
Zero-Round Active Learning0
Zero-shot Active Learning Using Self Supervised Learning0
Extended Active Learning Method0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles0
Coresets for Classification -- Simplified and Strengthened0
Coresets for Classification – Simplified and Strengthened0
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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