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 1851–1875 of 3073 papers

TitleStatusHype
Detecting Repeating Objects using Patch Correlation Analysis—0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures—0
Dialog Policy Learning for Joint Clarification and Active Learning Queries—0
Diameter-Based Active Learning—0
Diameter-based Interactive Structure Discovery—0
Differentiable Submodular Maximization—0
Difficult Cases: From Data to Learning, and Back—0
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography—0
Diffusion-based Deep Active Learning—0
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Direct Acquisition Optimization for Low-Budget Active Learning—0
DIRECT: Deep Active Learning under Imbalance and Label Noise—0
Dirichlet Active Learning—0
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation—0
Discovering and forecasting extreme events via active learning in neural operators—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
Discriminative Active Learning for Domain Adaptation—0
Discriminative Batch Mode Active Learning—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—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