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 151–175 of 3073 papers

TitleStatusHype
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Active Learning Enables Extrapolation in Molecular Generative Models—0
Bayesian Active Learning By Distribution DisagreementCode0
Joint Out-of-Distribution Filtering and Data Discovery Active Learning—0
Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning—0
U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario—0
ACIL: Active Class Incremental Learning for Image Classification—0
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Active Learning with Variational Quantum Circuits for Quantum Process Tomography—0
Uncertainty Herding: One Active Learning Method for All Label Budgets—0
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
Image Classification with Deep Reinforcement Active Learning—0
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object DetectionCode0
Uncertainty Quantification in Continual Open-World Learning—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation—0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point SupervisionCode3
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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