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 21912200 of 3073 papers

TitleStatusHype
Noise-tolerant, Reliable Active Classification with Comparison Queries0
Payoff Information and Learning in Signaling Games0
Unsupervised Pool-Based Active Learning for Linear RegressionCode0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift0
What is the Value of Data? On Mathematical Methods for Data Quality Estimation0
Domain-independent Extraction of Scientific Concepts from Research ArticlesCode0
LTP: A New Active Learning Strategy for CRF-Based Named Entity RecognitionCode1
Fair Active LearningCode0
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction0
Learning the Valuations of a k-demand Agent0
Show:102550
← PrevPage 220 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