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 751–775 of 3073 papers

TitleStatusHype
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
A Bayesian Active Learning Approach to Comparative Judgement—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Overcoming Overconfidence for Active LearningCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
AI For Fraud Awareness—0
Classification Committee for Active Deep Object Detection—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
Applied metamodelling for ATM performance simulations—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—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