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 976–1000 of 3073 papers

TitleStatusHype
Algorithm Selection for Deep Active Learning with Imbalanced DatasetsCode0
Investigating Multi-source Active Learning for Natural Language InferenceCode0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play—0
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Best Practices in Active Learning for Semantic Segmentation—0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data—0
Combining self-labeling and demand based active learning for non-stationary data streams—0
Continuous Learning for Android Malware DetectionCode1
AutoWS: Automated Weak Supervision Framework for Text Classification—0
An Informative Path Planning Framework for Active Learning in UAV-based Semantic MappingCode1
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
SAAL: Sharpness-Aware Active LearningCode1
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty ModelingCode0
Robust online active learning—0
Does Deep Active Learning Work in the Wild?—0
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Active Learning for Multilingual Semantic Parser—0
Identifying Adversarially Attackable and Robust SamplesCode0
Leveraging Importance Weights in Subset Selection—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators—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