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 27212730 of 3073 papers

TitleStatusHype
Active Few-Shot Learning with FASLCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
The Label Complexity of Active Learning from Observational DataCode0
Prioritizing Informative Features and Examples for Deep Learning from Noisy DataCode0
Domain Adaptation from ScratchCode0
Stream-based Active Learning with Verification Latency in Non-stationary EnvironmentsCode0
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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