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 1076–1100 of 3073 papers

TitleStatusHype
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing—0
Active Learning with Expected Error Reduction—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Addressing Limited Data for Textual Entailment Across Domains—0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Active Learning Algorithms for Graphical Model Selection—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Active Learning for Phenotyping Tasks—0
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)—0
Adaptivity to Noise Parameters in Nonparametric Active Learning—0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Convergence of Uncertainty Sampling for Active Learning—0
Coresets for Classification – Simplified and Strengthened—0
Adaptivity in Adaptive Submodularity—0
Adaptive Submodular Ranking and Routing—0
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization—0
Active learning for imbalanced data under cold start—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