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
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
Learning a Policy for Opportunistic Active Learning—0
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement LearningCode0
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
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
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
Active DOP: A constituency treebank annotation tool with online learningCode0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive AnnotationCode0
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Sprucing up the trees -- Error detection in treebanks—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
A Structured Perspective of Volumes on Active Learning—0
Noise Contrastive Priors for Functional UncertaintyCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—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