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 1701–1725 of 3073 papers

TitleStatusHype
Importance sampling based active learning for parametric seismic fragility curve estimation—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Active Learning for Automated Visual Inspection of Manufactured Products—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
Sample Noise Impact on Active LearningCode0
ALLWAS: Active Learning on Language models in WASserstein space—0
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Word Discriminations for Vocabulary Inventory PredictionCode0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text—0
Headnote Prediction Using Machine Learning—0
TAR on Social Media: A Framework for Online Content ModerationCode0
Certifying One-Phase Technology-Assisted Reviews—0
Reducing Label Effort: Self-Supervised meets Active Learning—0
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays—0
Region-level Active Detector Learning—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning—0
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation—0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
A Comparison of Strategies for Source-Free Domain Adaptation—0
Neural Predictive Monitoring under Partial ObservabilityCode0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—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