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 1526–1550 of 3073 papers

TitleStatusHype
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—0
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection—0
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning—0
Structural query-by-committee—0
Structured Prediction via Learning to Search under Bandit Feedback—0
Structuring Operative Notes using Active Learning—0
Submodularity Cuts and Applications—0
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings—0
Submodular Learning and Covering with Response-Dependent Costs—0
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz—0
Submodular Mutual Information for Targeted Data Subset Selection—0
Subsequence Based Deep Active Learning for Named Entity Recognition—0
Subspace Clustering with Active Learning—0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems—0
Sufficient Conditions for Agnostic Active Learnable—0
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation—0
SUPClust: Active Learning at the Boundaries—0
Superposition through Active Learning lens—0
Supervised Negative Binomial Classifier for Probabilistic Record Linkage—0
Supervising Feature Influence—0
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection—0
Support Vector Machines under Adversarial Label Contamination—0
Surrogate Losses in Passive and Active Learning—0
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework—0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization—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