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 581–590 of 3073 papers

TitleStatusHype
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi—0
Inconsistency-Based Data-Centric Active Open-Set AnnotationCode1
The Role of Higher-Order Cognitive Models in Active Learning—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Zero-shot Active Learning Using Self Supervised Learning—0
Weakly Supervised Point Cloud Semantic Segmentation via Artificial OracleCode1
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—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