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 13311340 of 3073 papers

TitleStatusHype
Discovering and forecasting extreme events via active learning in neural operators0
On Efficiently Acquiring Annotations for Multilingual ModelsCode0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning0
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
AKF-SR: Adaptive Kalman Filtering-based Successor Representation0
Efficient Active Learning with Abstention0
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Self-supervised 360^ Room Layout EstimationCode0
Near-optimality for infinite-horizon restless bandits with many arms0
Evolving Multi-Label Fuzzy Classifier0
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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