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 851–875 of 3073 papers

TitleStatusHype
Batch Decorrelation for Active Metric LearningCode0
Bayesian Active Learning By Distribution DisagreementCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
Batch Active Preference-Based Learning of Reward FunctionsCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Bayesian Dark KnowledgeCode0
Quantifying Local Model Validity using Active LearningCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Active Selection of Classification FeaturesCode0
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning PrincipleCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Automated Performance Testing Based on Active Deep LearningCode0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain ShiftCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Noise Contrastive Priors for Functional UncertaintyCode0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
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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