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 2851–2875 of 3073 papers

TitleStatusHype
Active Learning for Delineation of Curvilinear Structures—0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning—0
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching—0
Convergence rates of sub-sampled Newton methods—0
Context Aware Active Learning of Activity Recognition Models—0
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond—0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained RecognitionCode0
QBDC: Query by dropout committee for training deep supervised architecture—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Fine-Grained Product Class Recognition for Assisted Shopping—0
Active Learning from Weak and Strong Labelers—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Learning in Unlabeled Networks - An Active Learning and Inference Approach—0
Distance-Penalized Active Learning Using Quantile Search—0
Incremental Active Opinion Learning Over a Stream of Opinionated Documents—0
Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)—0
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Improving Event Detection with Active Learning—0
Efficient Named Entity Annotation through Pre-empting—0
Multi-armed Bandit Problem with Known Trend—0
Introducing Geometry in Active Learning for Image Segmentation—0
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles—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