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 701–725 of 3073 papers

TitleStatusHype
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation—0
Improved Active Learning via Dependent Leverage Score Sampling—0
Guideline Learning for In-context Information Extraction—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug DesignCode1
Pool-Based Active Learning with Proper Topological Regions—0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Deep Active Learning with Noisy Oracle in Object Detection—0
Nonparametric active learning for cost-sensitive classification—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Towards Free Data Selection with General-Purpose ModelsCode1
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Gradient and Uncertainty Enhanced Sequential Sampling for Global FitCode0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Frugal Satellite Image Change Detection with Deep-Net Inversion—0
Online Active Learning For Sound Event Detection—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Explaining Predictive Uncertainty with Information Theoretic Shapley ValuesCode1
Evidential uncertainties on rich labels for active learning—0
Active Learning for Multilingual Fingerspelling Corpora—0
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening—0
Active anomaly detection based on deep one-class classification—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