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 12911300 of 3073 papers

TitleStatusHype
Probability trees and the value of a single intervention0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling0
Active learning of causal probability trees0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data0
Self-supervised Assisted Active Learning for Skin Lesion SegmentationCode1
ALLSH: Active Learning Guided by Local Sensitivity and Hardness0
Towards Computationally Feasible Deep Active LearningCode1
The Right Tool for the Job: Matching Active Learning Techniques to Learning ObjectivesCode0
One Size Does Not Fit All: The Case for Personalised Word Complexity Models0
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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