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 2401–2425 of 3073 papers

TitleStatusHype
Active Learning for Skewed Data Sets—0
Active Learning for Sound Event Detection—0
Active Learning for Speech Recognition: the Power of Gradients—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Active Learning for Saddle Point Calculation—0
Active Learning for Undirected Graphical Model Selection—0
Active Learning for Video Classification with Frame Level Queries—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Active Learning for Vision-Language Models—0
Active Learning for Wireless IoT Intrusion Detection—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning from Crowd in Document Screening—0
Active Learning from Imperfect Labelers—0
Active Learning from Peers—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Active Learning from Weak and Strong Labelers—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—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