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 1401–1425 of 3073 papers

TitleStatusHype
Active Learning Over Multiple Domains in Natural Language Tasks—0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times—0
Towards Robust Deep Active Learning for Scientific Computing—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
Competition over data: how does data purchase affect users?—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
How Low Can We Go? Pixel Annotation for Semantic Segmentation—0
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning—0
Optimal Data Selection: An Online Distributed ViewCode0
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI—0
Cold Start Active Learning Strategies in the Context of Imbalanced Classification—0
Active Learning Polynomial Threshold Functions—0
Analytic Mutual Information in Bayesian Neural Networks—0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning—0
Partition-Based Active Learning for Graph Neural NetworksCode0
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization—0
Batch versus Sequential Active Learning for Recommender Systems—0
PT4AL: Using Self-Supervised Pretext Tasks for Active LearningCode1
Optimizing Active Learning for Low Annotation Budgets—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