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 871880 of 3073 papers

TitleStatusHype
Active Selection of Classification FeaturesCode0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Revisiting Sample Size Determination in Natural Language UnderstandingCode0
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
RISAN: Robust Instance Specific Abstention NetworkCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Batch Active Learning at ScaleCode0
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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