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 25512560 of 3073 papers

TitleStatusHype
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS0
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling0
Making Efficient Use of a Domain Expert's Time in Relation Extraction0
Practical Obstacles to Deploying Active Learning0
Evaluating Active Learning Heuristics for Sequential Diagnosis0
Towards more Reliable Transfer Learning0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification0
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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