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 731740 of 3073 papers

TitleStatusHype
Classification Tree-based Active Learning: A Wrapper Approach0
Active Learning for Control-Oriented Identification of Nonlinear Systems0
Experimental Design for Active Transductive Inference in Large Language Models0
Interactive Ontology Matching with Cost-Efficient Learning0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth0
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
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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