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 18511860 of 3073 papers

TitleStatusHype
Detecting Repeating Objects using Patch Correlation Analysis0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows0
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures0
Dialog Policy Learning for Joint Clarification and Active Learning Queries0
Diameter-Based Active Learning0
Diameter-based Interactive Structure Discovery0
Differentiable Submodular Maximization0
Difficult Cases: From Data to Learning, and Back0
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography0
Diffusion-based Deep Active Learning0
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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