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 381390 of 3073 papers

TitleStatusHype
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Continual egocentric object recognitionCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Confidence Estimation Using Unlabeled DataCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Compute-Efficient Active LearningCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
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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