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 2951–2975 of 3073 papers

TitleStatusHype
Incremental Activity Modeling and Recognition in Streaming Videos—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Improving Classification-Based Natural Language Understanding with Non-Expert Annotation—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Structuring Operative Notes using Active Learning—0
Difficult Cases: From Data to Learning, and Back—0
Design of an Active Learning System with Human Correction for Content Analysis—0
Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Constrained Topic Model—0
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Language Resource Addition: Dictionary or Corpus?—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
Focusing Annotation for Semantic Role Labeling—0
Active Learning for Undirected Graphical Model Selection—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Confidence-based Active Learning Methods for Machine Translation—0
Domain Adaptation with Active Learning for Coreference Resolution—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
Near Optimal Bayesian Active Learning for Decision Making—0
Selective Sampling with Drift—0
Human Activity Recognition using Smartphone—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