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

Showing 26512675 of 3073 papers

TitleStatusHype
DeMuX: Data-efficient Multilingual LearningCode0
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense RetrievalCode0
The Future of Data Science EducationCode0
Multilingual Detection of Personal Employment Status on TwitterCode0
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital HumanitiesCode0
Sample Efficient Learning of Predictors that Complement HumansCode0
Derivative free optimization via repeated classificationCode0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Sample Noise Impact on Active LearningCode0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Detecting value-expressive text posts in Russian social mediaCode0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Multitask Active Learning for Graph Anomaly DetectionCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Sampling and Reconstruction of Signals on Product GraphsCode0
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource SettingsCode0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified