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 3051–3073 of 3073 papers

TitleStatusHype
Data Shapley Valuation for Efficient Batch Active Learning—0
Data Summarization via Bilevel Optimization—0
Data Uncertainty without Prediction Models—0
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias—0
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning—0
DECAL: DEployable Clinical Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Deep Active Ensemble Sampling For Image Classification—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning: A Reality Check—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep Active Learning by Leveraging Training Dynamics—0
Deep Active Learning by Model Interpretability—0
Deep Active Learning for Computer Vision: Past and Future—0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Deep Active Learning for Dialogue Generation—0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Deep active learning for nonlinear system identification—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Deep Active Learning for Remote Sensing Object 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