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 2026–2050 of 3073 papers

TitleStatusHype
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps—0
Data-efficient Online Classification with Siamese Networks and Active Learning—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning—0
Online Learning of Non-Markovian Reward Models—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Mean-Variance Analysis in Bayesian Optimization under Uncertainty—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks—0
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Wireless for Machine Learning—0
A Survey of Deep Active LearningCode0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—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