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 1101–1125 of 3073 papers

TitleStatusHype
Active learning for deep semantic parsing—0
ActiveDP: Bridging Active Learning and Data Programming—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Automatic Playtesting for Game Parameter Tuning via Active Learning—0
Active Learning with Importance Sampling—0
Active Learning for Deep Object Detection—0
Automatic Learning to Detect Concept Drift—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Active Learning with Expert Advice—0
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
A critical look at the current train/test split in machine learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—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