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 2426–2450 of 3073 papers

TitleStatusHype
An Atomistic Machine Learning Package for Surface Science and CatalysisCode0
Active Multi-Information Source Bayesian Quadrature—0
Active Stacking for Heart Rate Estimation—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Active Learning of Spin Network ModelsCode0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Explanation-Based Attention for Semi-Supervised Deep Active Learning—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Scene Learning—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—0
Active Transfer Learning for Persian Offline Signature Verification—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses—0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Information Losses in Neural Classifiers from Sampling—0
K-nn active learning under local smoothness condition—0
Active Learning for High-Dimensional Binary Features—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Learning Linear Dynamical Systems with Semi-Parametric Least SquaresCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
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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