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 28912900 of 3073 papers

TitleStatusHype
Bias-Aware Heapified Policy for Active Learning0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora0
Bilingual Transfer Learning for Online Product Classification0
Boosting API Recommendation with Implicit Feedback0
Boosting Robustness Verification of Semantic Feature Neighborhoods0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching0
Boosting the Learning for Ranking Patterns0
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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