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 25112520 of 3073 papers

TitleStatusHype
Cost-Sensitive Reference Pair Encoding for Multi-Label LearningCode0
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active LearningCode0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learningCode0
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to ReviewersCode0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Continual egocentric object recognitionCode0
Mindful Active LearningCode0
Show:102550
← PrevPage 252 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