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 2126–2150 of 3073 papers

TitleStatusHype
Headnote Prediction Using Machine Learning—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition—0
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking—0
Heuristic Stopping Rules For Technology-Assisted Review—0
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance—0
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
Hierarchical Subquery Evaluation for Active Learning on a Graph—0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection—0
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation—0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks—0
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex—0
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users—0
Highly Efficient Regression for Scalable Person Re-Identification—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools—0
How Low Can We Go? Pixel Annotation for Semantic Segmentation—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?—0
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget—0
Practical Obstacles to Deploying Active Learning—0
HS^2: Active Learning over Hypergraphs—0
Human Active Learning—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