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 2676–2700 of 3073 papers

TitleStatusHype
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
An Active Learning Framework for Efficient Robust Policy Search—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
An active learning framework for multi-group mean estimation—0
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
An active learning model to classify animal species in Hong Kong—0
An Active Parameter Learning Approach to The Identification of Safe Regions—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Active Learning of Driving Scenario Trajectories—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Analysis of Stopping Active Learning based on Stabilizing Predictions—0
Analytic Mutual Information in Bayesian Neural Networks—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Analyzing Well-Formedness of Syllables in Japanese Sign Language—0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts—0
An Analysis of Active Learning With Uniform Feature Noise—0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—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