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 1131–1140 of 3073 papers

Title	Date	Tasks	Status
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
Self-Correcting Bayesian Optimization through Bayesian Active Learning	Apr 21, 2023	Active LearningBayesian Optimization	—Unverified
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning	Apr 19, 2023	Active LearningActivity Recognition	—Unverified
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning	Apr 18, 2023	Active Learningreinforcement-learning	—Unverified
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling	Apr 16, 2023	Active Learningregression	—Unverified
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps	Apr 15, 2023	Active LearningMultiobjective Optimization	CodeCode Available
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies	Apr 13, 2023	Active Learningimage-classification	—Unverified
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification	Apr 12, 2023	Active LearningDialogue Act Classification	—Unverified
Controllable Textual Inversion for Personalized Text-to-Image Generation	Apr 11, 2023	Active LearningImage Generation	CodeCode Available

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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