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 22212230 of 3073 papers

TitleStatusHype
Sample Efficient Active Learning of Causal Trees0
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning0
Merging Weak and Active Supervision for Semantic ParsingCode0
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach0
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
A User Study of Perceived Carbon Footprint0
Actively Learning Gaussian Process DynamicsCode0
Active Learning for Deep Detection Neural NetworksCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
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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