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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1710 of 3073 papers

TitleStatusHype
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
A critical look at the current train/test split in machine learning—0
Targeted Active Learning for Bayesian Decision-Making—0
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular DesignCode1
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Visual Transformer for Task-aware Active LearningCode1
Neural Active Learning with Performance Guarantees—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
Active Covering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified