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 1576–1600 of 3073 papers

TitleStatusHype
Data Summarization via Bilevel Optimization—0
Bayesian Active Learning for Sim-to-Real Robotic Perception—0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
Active Learning for Argument Strength EstimationCode0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsCode0
Towards Overcoming Practical Obstacles to Deploying Deep Active Learning—0
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks—0
FOMO: Topics versus documents in legal eDiscovery—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Mitigating Sampling Bias and Improving Robustness in Active Learning—0
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
Adaptive network reliability analysis: Methodology and applications to power grid—0
Open-World Active Learning with Stacking Ensemble for Self-Driving Cars—0
Active learning for reducing labeling effort in text classification tasksCode0
Cartography Active LearningCode1
Importance sampling based active learning for parametric seismic fragility curve estimation—0
Active Learning by Acquiring Contrastive ExamplesCode1
AdjointNet: Constraining machine learning models with physics-based codes—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning—0
Active Learning for Automated Visual Inspection of Manufactured Products—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Sample Noise Impact on Active LearningCode0
Word Discriminations for Vocabulary Inventory PredictionCode0
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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