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 16811690 of 3073 papers

TitleStatusHype
Para-active learning0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems0
Parameter-Efficient Active Learning for Foundational models0
Parameter Filter-based Event-triggered Learning0
Learning the Pareto Front Using Bootstrapped Observation Samples0
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation0
Parsimonious Random Vector Functional Link Network for Data Streams0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings0
Show:102550
← PrevPage 169 of 308Next →

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