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 3051–3073 of 3073 papers

TitleStatusHype
Bayesian Bias Mitigation for Crowdsourcing—0
Online Submodular Set Cover, Ranking, and Repeated Active Learning—0
Active learning of neural response functions with Gaussian processes—0
Agnostic Active Learning Without Constraints—0
Extensions of Generalized Binary Search to Group Identification and Exponential Costs—0
Active Instance Sampling via Matrix Partition—0
Multi-View Active Learning in the Non-Realizable Case—0
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning—0
Active Learning by Querying Informative and Representative Examples—0
Extended Active Learning Method—0
Near-Optimal Bayesian Active Learning with Noisy Observations—0
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization—0
Submodularity Cuts and Applications—0
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition—0
Sufficient Conditions for Agnostic Active Learnable—0
Noisy Generalized Binary Search—0
Learning to Explore and Exploit in POMDPs—0
Human Active Learning—0
Multiple-Instance Active Learning—0
Active Preference Learning with Discrete Choice Data—0
Discriminative Batch Mode Active Learning—0
Active Learning with Statistical Models—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