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

Showing 15311540 of 3073 papers

TitleStatusHype
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project0
Modulation and signal class labelling using active learning and classification using machine learning0
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks0
Molecular Dynamics with Neural-Network Potentials0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild0
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning0
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias0
Morphological classification of astronomical images with limited labelling0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified