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 911–920 of 3073 papers

Title	Date	Tasks	Status	Hype
OpenAL: Evaluation and Interpretation of Active Learning Strategies	Apr 11, 2023	Active Learning	CodeCode Available	0
Deep Active Alignment of Knowledge Graph Entities and Schemata	Apr 10, 2023	Active LearningDeep Learning	CodeCode Available	0
Towards Active Learning for Action Spotting in Association Football Videos	Apr 9, 2023	Action SpottingActive Learning	—Unverified	0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction	Apr 7, 2023	Active LearningPrediction	—Unverified	0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction	Apr 6, 2023	Active Learningregression	—Unverified	0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks	Apr 5, 2023	Active Learning	—Unverified	0
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments	Apr 5, 2023	Active LearningRecommendation Systems	CodeCode Available	1
Incorporating Unlabelled Data into Bayesian Neural Networks	Apr 4, 2023	Active LearningSelf-Supervised Learning	—Unverified	0
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks	Apr 3, 2023	Active LearningQuestion Answering	CodeCode Available	1
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation	Apr 3, 2023	Active LearningTransfer Learning	—Unverified	0

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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