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 29512960 of 3073 papers

TitleStatusHype
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems0
Improving Classification-Based Natural Language Understanding with Non-Expert Annotation0
Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis0
Semantics for Large-Scale Multimedia: New Challenges for NLP0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy0
Difficult Cases: From Data to Learning, and Back0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes0
Incremental Activity Modeling and Recognition in Streaming Videos0
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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