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 3026–3050 of 3073 papers

TitleStatusHype
Bayesian active learning with localized priors for fast receptive field characterization—0
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature—0
Active Learning and the Irish Treebank—0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation—0
Multilabel Classification using Bayesian Compressed Sensing—0
Active and passive learning of linear separators under log-concave distributions—0
Active Learning for Crowd-Sourced Databases—0
Efficient Active Learning of Halfspaces: an Aggressive Approach—0
Surrogate Losses in Passive and Active Learning—0
Active Learning with Transfer Learning—0
Active Learning for Imbalanced Sentiment Classification—0
Batch Active Learning via Coordinated Matching—0
Active Learning for Coreference Resolution—0
Exploring Label Dependency in Active Learning for Phenotype Mapping—0
UPM system for WMT 2012—0
Active Learning for Coreference Resolution—0
DutchSemCor: Targeting the ideal sense-tagged corpus—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Distribution-Dependent Sample Complexity of Large Margin Learning—0
Active learning for interactive machine translation—0
Bayesian Active Learning for Classification and Preference LearningCode0
Video Annotation and Tracking with Active Learning—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Lower Bounds for Passive and Active Learning—0
Active Learning with a Drifting Distribution—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