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 2051–2075 of 3073 papers

TitleStatusHype
Deep Active Learning via Open Set RecognitionCode0
Active learning of timed automata with unobservable resets—0
Confidence-Aware Learning for Deep Neural NetworksCode1
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Motor cortex mapping using active gaussian processes—0
Functional MRI applications for psychiatric disease subtyping: a review—0
Sampling from a k-DPP without looking at all items—0
Similarity Search for Efficient Active Learning and Search of Rare Concepts—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Practical applications of metric space magnitude and weighting vectors—0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
Graph Policy Network for Transferable Active Learning on GraphsCode1
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Open Source Software for Efficient and Transparent ReviewsCode1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty QuantificationCode1
Effective Version Space Reduction for Convolutional Neural Networks—0
Fair Active LearningCode0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Active Learning for Nonlinear System Identification with Guarantees—0
Sequential Graph Convolutional Network for Active LearningCode1
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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