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 826–850 of 3073 papers

TitleStatusHype
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Active Learning under Label Shift—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Active learning for data streams: a survey—0
ALARM: Active LeArning of Rowhammer Mitigations—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning—0
Information Losses in Neural Classifiers from Sampling—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active Learning for Video Classification with Frame Level Queries—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
Active Learning for Coreference Resolution—0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
ALANNO: An Active Learning Annotation System for Mortals—0
A Lagrangian Duality Approach to Active Learning—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Active Learning for Undirected Graphical Model Selection—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