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 2776–2800 of 3073 papers

TitleStatusHype
A Structured Perspective of Volumes on Active Learning—0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A Survey of Active Learning for Natural Language Processing—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A Survey of Latent Factor Models in Recommender Systems—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis—0
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 for data streams: a survey—0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—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
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors—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
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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