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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 811–820 of 3073 papers

TitleStatusHype
Active Preference Optimization for Sample Efficient RLHFCode0
Class-Balanced and Reinforced Active Learning on Graphs—0
Self-consistent Validation for Machine Learning Electronic Structure—0
Reinforcement Learning from Human Feedback with Active Queries—0
Role-Playing Simulation Games using ChatGPT—0
Active Few-Shot Fine-Tuning—0
Active Preference Learning for Large Language Models—0
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets—0
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
ActiveDP: Bridging Active Learning and Data Programming—0
Show:102550
← PrevPage 82 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified