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 21612170 of 3073 papers

TitleStatusHype
Hyperbolic Learning with Multimodal Large Language Models0
Towards Robust Deep Active Learning for Scientific Computing0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples0
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems0
ICE: Rapid Information Extraction Customization for NLP Novices0
Identifying News from Tweets0
Identifying Wrongly Predicted Samples: A Method for Active Learning0
Image and Video Mining through Online Learning0
Image Classification with Deep Reinforcement Active Learning0
Image Restoration with Point Spread Function Regularization and Active Learning0
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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