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 16211630 of 3073 papers

TitleStatusHype
Adding more data does not always help: A study in medical conversation summarization with PEGASUS0
Solving Multi-Arm Bandit Using a Few Bits of Communication0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning0
Physics-enhanced deep surrogates for partial differential equations0
An Interactive Visualization Tool for Understanding Active LearningCode0
Deep Unsupervised Active Learning on Learnable Graphs0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning0
Contextual Bayesian optimization with binary outputs0
Active Learning for Rumor Identification on Social Media0
Partial-Adaptive Submodular Maximization0
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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