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 29412950 of 3073 papers

TitleStatusHype
libact: Pool-based Active Learning in PythonCode0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Few-Shot Learning with Graph Neural NetworksCode0
Few-Shot Learning with Graph Neural NetworksCode0
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning PrincipleCode0
Finding Better Active Learners for Faster Literature ReviewsCode0
Finding Convincing Arguments Using Scalable Bayesian Preference LearningCode0
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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