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 12011210 of 3073 papers

TitleStatusHype
Combining self-labeling and demand based active learning for non-stationary data streams0
AutoWS: Automated Weak Supervision Framework for Text Classification0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty ModelingCode0
Robust online active learning0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Does Deep Active Learning Work in the Wild?0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion0
Identifying Adversarially Attackable and Robust SamplesCode0
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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