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 20812090 of 3073 papers

TitleStatusHype
Active Learning under Label Shift0
Active Crowd Counting with Limited Supervision0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation0
IALE: Imitating Active Learner EnsemblesCode0
Resource Aware Multifidelity Active Learning for Efficient Optimization0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification0
Meta-active Learning in Probabilistically-Safe Optimization0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesCode0
The Sample Complexity of Best-k Items Selection from Pairwise ComparisonsCode0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design0
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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