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 1501–1525 of 3073 papers

TitleStatusHype
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint—0
Episode-Based Active Learning with Bayesian Neural Networks—0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space—0
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Estimating Optimal Active Learning via Model Retraining Improvement—0
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Amortized nonmyopic active search via deep imitation learning—0
Evaluating Zero-cost Active Learning for Object Detection—0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Discovering and forecasting extreme events via active learning in neural operators—0
Evidential uncertainties on rich labels for active learning—0
Amortized Active Learning for Nonparametric Functions—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—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