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 1–25 of 3073 papers

TitleStatusHype
On the reusability of samples in active learningCode5
Active Learning for Neural PDE SolversCode5
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object DetectionCode4
Let's Verify Step by StepCode4
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point SupervisionCode3
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical ImagesCode2
Transductive Active Learning: Theory and ApplicationsCode2
Physics-informed active learning for accelerating quantum chemical simulationsCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
Human-in-the-Loop Large-Scale Predictive Maintenance of WorkstationsCode2
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theoryCode2
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical ImagesCode2
Multi-Fidelity Active Learning with GFlowNetsCode2
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
Active Generalized Category DiscoveryCode2
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
DCoM: Active Learning for All LearnersCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
POTATO: The Portable Text Annotation ToolCode2
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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