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 921930 of 3073 papers

TitleStatusHype
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costsCode0
Fairness-Aware Data Valuation for Supervised Learning0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection0
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need0
Deep Kernel Methods Learn Better: From Cards to Process Optimization0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions0
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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