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 1326–1350 of 3073 papers

TitleStatusHype
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Task-Aware Active Learning for Endoscopic Image AnalysisCode0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Discovering and forecasting extreme events via active learning in neural operators—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
Parameter Filter-based Event-triggered Learning—0
On Efficiently Acquiring Annotations for Multilingual ModelsCode0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
Efficient Active Learning with Abstention—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—0
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Self-supervised 360^ Room Layout EstimationCode0
Near-optimality for infinite-horizon restless bandits with many arms—0
Evolving Multi-Label Fuzzy Classifier—0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure—0
A Comparative Survey of Deep Active LearningCode1
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical ImagesCode2
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Semantic Segmentation with Active Semi-Supervised Learning—0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications—0
RareGAN: Generating Samples for Rare ClassesCode0
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learningCode0
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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