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 2351–2375 of 3073 papers

TitleStatusHype
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Learning for Graphs with Noisy Structures—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Human Pose Estimation—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Imbalanced Sentiment Classification—0
Active learning for interactive machine translation—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles—0
Active learning for interactive satellite image change detection—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Active learning for level set estimation under cost-dependent input uncertainty—0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Active learning for medical code assignment—0
Active Learning for Multi-class Image Classification—0
Active Learning for Multilingual Fingerspelling Corpora—0
Active Learning for Multilingual Semantic Parser—0
Active Learning for Natural Language Generation—0
Active Learning for Network Intrusion Detection—0
Active Learning for Network Traffic Classification: A Technical Study—0
Active Learning for New Domains in Natural Language Understanding—0
Active Learning for NLP with Large Language Models—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—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