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 251–275 of 3073 papers

TitleStatusHype
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical PerspectiveCode1
Counting People by Estimating People FlowsCode1
Uncertainty estimation for molecular dynamics and samplingCode1
Active Learning for BERT: An Empirical StudyCode1
Active Learning for Human-in-the-Loop Customs InspectionCode1
Cold-start Active Learning through Self-supervised Language ModelingCode1
Semi-supervised Batch Active Learning via Bilevel OptimizationCode1
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
OLALA: Object-Level Active Learning for Efficient Document Layout AnnotationCode1
SeqMix: Augmenting Active Sequence Labeling via Sequence MixupCode1
HUMAN: Hierarchical Universal Modular ANnotatorCode1
Neural BootstrapperCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Multi-task Causal Learning with Gaussian ProcessesCode1
Synbols: Probing Learning Algorithms with Synthetic DatasetsCode1
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneCode1
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
Contextual Diversity for Active LearningCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
On uncertainty estimation in active learning for image segmentationCode1
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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