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 451460 of 3073 papers

TitleStatusHype
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine TranslationCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Domain-independent Extraction of Scientific Concepts from Research ArticlesCode0
An Adversarial Objective for Scalable ExplorationCode0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Dual Active Sampling on Batch-Incremental Active LearningCode0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
Active Learning for Argument Strength EstimationCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
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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