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 976–1000 of 3073 papers

TitleStatusHype
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Causal Discovery and Prediction: Methods and Algorithms—0
Active anomaly detection based on deep one-class classification—0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls—0
Interactively Teaching an Inverse Reinforcement Learner with Limited FeedbackCode0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Semantic Parsing in Limited Resource Conditions—0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus ImagesCode0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Learning Objective-Specific Active Learning Strategies with Attentive Neural ProcessesCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization—0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry SystemCode0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning—0
On the Query Strategies for Efficient Online Active Distillation—0
Streaming Active Learning for Regression Problems Using Regression via Classification—0
Explainable Active Learning for Preference ElicitationCode0
LAMBO: Large AI Model Empowered Edge Intelligence—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
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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