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 28112820 of 3073 papers

TitleStatusHype
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imagingCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Actively Discovering New Slots for Task-oriented ConversationCode0
O-MedAL: Online Active Deep Learning for Medical Image AnalysisCode0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
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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