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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 891–900 of 3073 papers

Title	Date	Tasks	Status
Active Continual Learning: On Balancing Knowledge Retention and Learnability	May 6, 2023	Active LearningContinual Learning	—Unverified
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks	May 4, 2023	Active LearningDrug Design	—Unverified
Multi-Domain Learning From Insufficient Annotations	May 4, 2023	Active LearningContrastive Learning	—Unverified
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge	May 3, 2023	Active LearningImplicit Discourse Relation Classification	CodeCode Available
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles	May 2, 2023	Active LearningDomain Generalization	CodeCode Available
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development	May 2, 2023	Active LearningAI Agent	—Unverified
NewsPanda: Media Monitoring for Timely Conservation Action	Apr 30, 2023	Active LearningArticles	CodeCode Available
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings	Apr 28, 2023	Active Learningreinforcement-learning	—Unverified
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques	Apr 25, 2023	Active LearningAnomaly Detection	CodeCode Available
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs	Apr 24, 2023	Active Learning	—Unverified

Show:10 25 50

← PrevPage 90 of 308Next →

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified