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
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Active Learning for Dependency Parsing with Partial Annotation—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Batch Active Learning of Reward Functions from Human Preferences—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Batch Active Learning via Coordinated Matching—0
Active Learning with Simple Questions—0
Active learning for detection of stance components—0
Active Learning with Statistical Models—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Batch versus Sequential Active Learning for Recommender Systems—0
BayesFormer: Transformer with Uncertainty Estimation—0
Bayesian Active Edge Evaluation on Expensive Graphs—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Active Learning with TensorBoard Projector—0
Bayesian Active Learning for Censored Regression—0
Bayesian active learning for choice models with deep Gaussian processes—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
Bayesian Active Learning for Discrete Latent Variable Models—0
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—0
Active learning with version spaces for object detection—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Active Learning Approach to Optimization of Experimental Control—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