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 1851–1875 of 3073 papers

TitleStatusHype
Active learning for medical code assignment—0
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations—0
Active learning using weakly supervised signals for quality inspection—0
Low-Regret Active learning—0
Automated Performance Testing Based on Active Deep LearningCode0
Fast Design Space Exploration of Nonlinear Systems: Part II—0
Stopping Criterion for Active Learning Based on Error StabilityCode0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Optimal Sampling Gaps for Adaptive Submodular Maximization—0
Efficacy of Bayesian Neural Networks in Active LearningCode0
STARdom: an architecture for trusted and secure human-centered manufacturing systems—0
Paladin: an annotation tool based on active and proactive learning—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Towards Active Learning Based Smart Assistant for Manufacturing—0
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation—0
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries—0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Data driven semi-supervised learning—0
Learning Novel Objects Continually Through Curiosity—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—0
Continual Developmental Neurosimulation Using Embodied Computational AgentsCode0
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data—0
Feedback Coding for Active LearningCode0
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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