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 1351–1375 of 3073 papers

TitleStatusHype
Active learning machine learns to create new quantum experiments—0
Active Covering—0
An Analysis of Active Learning With Uniform Feature Noise—0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts—0
Active Learning in Video Tracking—0
Analyzing Well-Formedness of Syllables in Japanese Sign Language—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Active Learning Classification from a Signal Separation Perspective—0
Analytic Mutual Information in Bayesian Neural Networks—0
Analysis of Stopping Active Learning based on Stabilizing Predictions—0
MaxiMin Active Learning in Overparameterized Model Classes—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Active learning in the geometric block model—0
Active Learning by Querying Informative and Representative Examples—0
Active covariance estimation by random sub-sampling of variables—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
Coupled reaction and diffusion governing interface evolution in solid-state batteries—0
Active Learning of Driving Scenario Trajectories—0
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Active Learning in Symbolic Regression with Physical Constraints—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Active Learning by Query by Committee with Robust Divergences—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