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 851–875 of 3073 papers

TitleStatusHype
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
Bucketized Active Sampling for Learning ACOPF—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
Active Learning with Constrained Topic Model—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Auto-Differentiating Linear Algebra—0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—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 Expert Advice—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
AI For Fraud Awareness—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—0
Automatic Learning to Detect Concept Drift—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—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