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
Active Learning of Multi-Index Function Models—0
Multilabel Classification using Bayesian Compressed Sensing—0
Active Learning of Model Evidence Using Bayesian Quadrature—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 for Imbalanced Sentiment Classification—0
Active Learning with Transfer Learning—0
Batch Active Learning via Coordinated Matching—0
UPM system for WMT 2012—0
Exploring Label Dependency in Active Learning for Phenotype Mapping—0
Active Learning for Coreference Resolution—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
Active Learning with a Drifting Distribution—0
Active learning of neural response functions with Gaussian processes—0
Video Annotation and Tracking with Active Learning—0
Bayesian Bias Mitigation for Crowdsourcing—0
Online Submodular Set Cover, Ranking, and Repeated Active Learning—0
Lower Bounds for Passive and Active Learning—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