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 501–525 of 3073 papers

TitleStatusHype
Adaptive Submodular Ranking and Routing—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Learning on Synthons for Molecular Design—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Active Regression by Stratification—0
Active Regression via Linear-Sample Sparsification—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
Active Learning for Chinese Word Segmentation—0
Active Learning for Breast Cancer Identification—0
Active Learning on Medical Image—0
Active Learning Guided by Efficient Surrogate Learners—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
Active Learning on a Programmable Photonic Quantum Processor—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—0
Active learning for binary classification with variable selection—0
Active Deep Learning for Classification of Hyperspectral Images—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Active learning of timed automata with unobservable resets—0
Active learning of the thermodynamics-dynamics tradeoff in protein condensates—0
Active Learning of SVDD Hyperparameter Values—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