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 201–250 of 3073 papers

TitleStatusHype
Model-Change Active Learning in Graph-Based Semi-Supervised LearningCode1
Class-Balanced Active Learning for Image ClassificationCode1
Hitting the Target: Stopping Active Learning at the Cost-Based OptimumCode1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
Cartography Active LearningCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Fluent: An AI Augmented Writing Tool for People who StutterCode1
Influence Selection for Active LearningCode1
Multi-Anchor Active Domain Adaptation for Semantic SegmentationCode1
Semi-Supervised Active Learning with Temporal Output DiscrepancyCode1
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic SegmentationCode1
Revisiting Uncertainty-based Query Strategies for Active Learning with TransformersCode1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
SIMILAR: Submodular Information Measures Based Active Learning In Realistic ScenariosCode1
TableSense: Spreadsheet Table Detection with Convolutional Neural NetworksCode1
TagRuler: Interactive Tool for Span-Level Data Programming by DemonstrationCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
Quality-Aware Memory Network for Interactive Volumetric Image SegmentationCode1
On Minimizing Cost in Legal Document Review WorkflowsCode1
Gone Fishing: Neural Active Learning with Fisher EmbeddingsCode1
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular DesignCode1
Visual Transformer for Task-aware Active LearningCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?Code1
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Model Learning with Personalized Interpretability Estimation (ML-PIE)Code1
All you need are a few pixels: semantic segmentation with PixelPickCode1
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
Multiple instance active learning for object detectionCode1
Is segmentation uncertainty useful?Code1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Consistency-based Active Learning for Object DetectionCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Discrepancy-Based Active Learning for Domain AdaptationCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Deep Deterministic Uncertainty: A Simple BaselineCode1
SISE-PC: Semi-supervised Image Subsampling for Explainable PathologyCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksCode1
Towards Understanding the Behaviors of Optimal Deep Active Learning AlgorithmsCode1
GLISTER: Generalization based Data Subset Selection for Efficient and Robust LearningCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Show:102550
← PrevPage 5 of 62Next →

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