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 901–925 of 3073 papers

TitleStatusHype
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Batch Active Learning at ScaleCode0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Regression via Linear-Sample Sparsification—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Generative Adversarial Network for Image Classification—0
Active Regression by Stratification—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
Active Learning for Human Pose Estimation—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
Active Learning for High-Dimensional Binary Features—0
Active Preference Learning with Discrete Choice Data—0
Active Learning for Graphs with Noisy Structures—0
Active Preference Learning for Large Language Models—0
Active Player Modelling—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Active Perceptual Similarity Modeling with Auxiliary Information—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