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 251260 of 3073 papers

TitleStatusHype
Bayesian Model-Agnostic Meta-LearningCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Bayesian Optimization with Conformal Prediction SetsCode1
Active Learning from the WebCode1
Boosting Active Learning via Improving Test PerformanceCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
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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