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 2601–2625 of 3073 papers

TitleStatusHype
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Pool-Based Sequential Active Learning for RegressionCode0
Textual Membership QueriesCode0
Efficient active learning of sparse halfspaces—0
Bayesian active learning for choice models with deep Gaussian processes—0
modAL: A modular active learning framework for PythonCode0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—0
Active Learning for Breast Cancer Identification—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Active Learning for Efficient Testing of Student Programs—0
Derivative free optimization via repeated classificationCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Active covariance estimation by random sub-sampling of variables—0
Active Metric Learning for Supervised Classification—0
Supervising Feature Influence—0
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection—0
Handling Adversarial Concept Drift in Streaming Data—0
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval—0
Structural query-by-committee—0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach—0
Differentiable Submodular Maximization—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