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 2701–2725 of 3073 papers

TitleStatusHype
Active Heteroscedastic Regression—0
Interpretable Active LearningCode0
Learning Algorithms for Active Learning—0
A Survey on Multi-Task LearningCode0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model—0
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL—0
Predicting the Quality of Short Narratives from Social Media—0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
Active Sentiment Domain Adaptation—0
The Impact of Typicality for Informative Representative Selection—0
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks—0
Detecting annotation noise in automatically labelled data—0
Probabilistic Active Learning of Functions in Structural Causal Models—0
Bayesian Semisupervised Learning with Deep Generative Models—0
A Meta-Learning Approach to One-Step Active Learning—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
A Variance Maximization Criterion for Active LearningCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Nuclear Discrepancy for Active Learning—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Active learning machine learns to create new quantum experiments—0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
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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