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 11111120 of 3073 papers

TitleStatusHype
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users0
Active Learning Framework to Automate NetworkTraffic Classification0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation0
Worst-Case Adaptive Submodular Cover0
From colouring-in to pointillism: revisiting semantic segmentation supervision0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities0
Batch Multi-Fidelity Active Learning with Budget Constraints0
Learning General World Models in a Handful of Reward-Free Deployments0
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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