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 491500 of 3073 papers

TitleStatusHype
Computational Assessment of Hyperpartisanship in News TitlesCode0
Active Learning for Abstractive Text SummarizationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino HabitatsCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Compute-Efficient Active LearningCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Confidence Estimation Using Unlabeled DataCode0
Cooperative Inverse Reinforcement 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