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 26612670 of 3073 papers

TitleStatusHype
Adaptive Active Hypothesis Testing under Limited Information0
Active Regression via Linear-Sample Sparsification0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Bayesian Active Edge Evaluation on Expensive Graphs0
Variational Adaptive-Newton Method for Explorative Learning0
Few-Shot Learning with Graph Neural NetworksCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Online Tool Condition Monitoring Based on Parsimonious Ensemble+0
Deep Active Learning over the Long Tail0
Show:102550
← PrevPage 267 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