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 651–675 of 3073 papers

TitleStatusHype
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning from Imperfect Labelers—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning from Peers—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
ALLSH: Active Learning Guided by Local Sensitivity and Hardness—0
ALLWAS: Active Learning on Language models in WASserstein space—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
An Efficient Active Learning Framework for New Relation Types—0
ALPINE: Active Link Prediction using Network Embedding—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling—0
ALVIN: Active Learning Via INterpolation—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
A Machine-learning framework for automatic reference-free quality assessment in MRI—0
A Markovian Formalism for Active Querying—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—0
Active Learning for Wireless IoT Intrusion Detection—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