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 23912400 of 3073 papers

TitleStatusHype
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Minimum-Margin Active Learning0
Learning by Active Nonlinear Diffusion0
Understanding Goal-Oriented Active Learning via Influence Functions0
Training Data Subset Search with Ensemble Active Learning0
MaxiMin Active Learning in Overparameterized Model Classes0
The Label Complexity of Active Learning from Observational DataCode0
Correlation Clustering with Adaptive Similarity QueriesCode0
Dual Active Sampling on Batch-Incremental Active LearningCode0
A framework for the extraction of Deep Neural Networks by leveraging public data0
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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