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 23612370 of 3073 papers

TitleStatusHype
'In-Between' Uncertainty in Bayesian Neural Networks0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree0
Active Learning Solution on Distributed Edge Computing0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout0
Flattening a Hierarchical Clustering through Active Learning0
Regional based query in graph active learningCode0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
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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