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 601–625 of 3073 papers

TitleStatusHype
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Detecting value-expressive text posts in Russian social mediaCode0
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming—0
DIRECT: Deep Active Learning under Imbalance and Label Noise—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Fair Active Learning in Low-Data Regimes—0
Active learning with biased non-response to label requests—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Semi-supervised Active Learning for Video Action DetectionCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
Transferable Candidate Proposal with Bounded UncertaintyCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path PlanningCode1
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
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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