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 2751–2775 of 3073 papers

TitleStatusHype
Diameter-Based Active Learning—0
Active Learning Using Uncertainty InformationCode0
Generative Adversarial Active Learning—0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning—0
Learning to Multi-Task by Active SamplingCode0
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces—0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization—0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
DroidStar: Callback Typestates for Android Classes—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification—0
Active Learning and Proofreading for Delineation of Curvilinear Structures—0
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes—0
Knowledge Completion for Generics using Guided Tensor Factorization—0
Deep Active Learning for Dialogue Generation—0
Finding Better Active Learners for Faster Literature ReviewsCode0
Active Learning for Speech Recognition: the Power of Gradients—0
Factored Contextual Policy Search with Bayesian Optimization—0
Highly Efficient Regression for Scalable Person Re-Identification—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
Active learning for detection of stance components—0
Align Me: A framework to generate Parallel Corpus Using OCRs and Bilingual Dictionaries—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