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 10211030 of 3073 papers

TitleStatusHype
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling0
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping0
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLPCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
A Pre-trained Data Deduplication Model based on Active Learning0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Uncertainty in Natural Language Generation: From Theory to Applications0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
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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