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 2526–2550 of 3073 papers

TitleStatusHype
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and SummarizationCode0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems—0
Events Beyond ACE: Curated Training for Events—0
Query-Efficient Black-Box Attack by Active Learning—0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Learning Time Dependent Choice—0
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models—0
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection—0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization—0
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement LearningCode0
Learning a Policy for Opportunistic Active Learning—0
Adversarial Sampling for Active Learning—0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study—0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
Active Learning for Regression Using Greedy SamplingCode0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
Active Learning for Wireless IoT Intrusion Detection—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Active DOP: A constituency treebank annotation tool with online learningCode0
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive AnnotationCode0
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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