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 2626–2650 of 3073 papers

TitleStatusHype
A framework for the extraction of Deep Neural Networks by leveraging public data—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Deep Active Learning for Anomaly Detection—0
Active learning for structural reliability: survey, general framework and benchmark—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Agnostic Active Learning Without Constraints—0
Agnostic Multi-Group Active Learning—0
A Graph-Based Approach for Active Learning in Regression—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
AI For Fraud Awareness—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—0
A Lagrangian Duality Approach to Active Learning—0
ALANNO: An Active Learning Annotation System for Mortals—0
ALARM: Active LeArning of Rowhammer Mitigations—0
ALdataset: a benchmark for pool-based active learning—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Show:102550
← PrevPage 106 of 123Next →

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