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 971980 of 3073 papers

TitleStatusHype
Gaussian Switch Sampling: A Second Order Approach to Active LearningCode0
Robust expected improvement for Bayesian optimization0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles0
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
Adaptive Selective Sampling for Online Prediction with Experts0
Algorithm Selection for Deep Active Learning with Imbalanced DatasetsCode0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
Investigating Multi-source Active Learning for Natural Language InferenceCode0
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play0
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
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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