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 801–825 of 3073 papers

TitleStatusHype
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
ALdataset: a benchmark for pool-based active learning—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
A Structured Perspective of Volumes on Active Learning—0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
A Survey of Latent Factor Models in Recommender Systems—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—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