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

Showing 26812690 of 3073 papers

TitleStatusHype
Anomaly Detection in Hierarchical Data Streams under Unknown Models0
An Eye-tracking Study of Named Entity Annotation0
Experiments in Non-Coherent Post-editing0
Structured Prediction via Learning to Search under Bandit Feedback0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts0
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input SpaceCode0
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking0
Actively Learning what makes a Discrete Sequence Valid0
Gradient Methods for Submodular Maximization0
Show:102550
← PrevPage 269 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified