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

Showing 451460 of 3073 papers

TitleStatusHype
Interactive Ontology Matching with Cost-Efficient Learning0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth0
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Focused Active Learning for Histopathological Image Classification0
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imagingCode0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified