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 1201–1225 of 3073 papers

TitleStatusHype
Combining self-labeling and demand based active learning for non-stationary data streams—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty ModelingCode0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
Robust online active learning—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Does Deep Active Learning Work in the Wild?—0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Identifying Adversarially Attackable and Robust SamplesCode0
Active Learning for Multilingual Semantic Parser—0
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
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling—0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning—0
Active Learning of Piecewise Gaussian Process Surrogates—0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
TAAL: Test-time Augmentation for Active Learning in Medical Image SegmentationCode0
Scalable Batch Acquisition for Deep Bayesian Active LearningCode0
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data—0
Combining Self-labeling with Selective Sampling—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