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 21412150 of 3073 papers

TitleStatusHype
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)0
Data-driven surrogate modelling and benchmarking for process equipment0
An Adversarial Objective for Scalable ExplorationCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning ModelsCode0
Modelling Human Active Search in Optimizing Black-box Functions0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment0
An Active Learning Framework for Constructing High-fidelity Mobility Maps0
Interactive Robot Training for Non-Markov Tasks0
Model Assertions for Monitoring and Improving ML ModelsCode1
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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