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 1276–1300 of 3073 papers

TitleStatusHype
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories—0
Active Learning of Multi-Index Function Models—0
基於多模態主動式學習法進行需備標記樣本之挑選用於候用校長評鑑之自動化評分系統建置(A Multimodal Active Learning Approach toward Identifying Samples to Label during the Development of Automatic Oral Presentation Assessment System for Pre-service Principals Certification Program)[In Chinese]—0
Active Learning for Argument Mining: A Practical Approach—0
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning—0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
Active Learning of Model Evidence Using Bayesian Quadrature—0
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks—0
Active Learning of Mealy Machines with Timers—0
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty—0
Active Deep Decoding of Linear Codes—0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning for affinity prediction of antibodies—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Active Learning for Accurate Estimation of Linear Models—0
Show:102550
← PrevPage 52 of 123Next →

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