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 1051–1060 of 3073 papers

Title	Date	Tasks	Status	Hype
Frugal Reinforcement-based Active Learning	Dec 9, 2022	Active LearningDiversity	—Unverified	0
Evaluating Zero-cost Active Learning for Object Detection	Dec 8, 2022	Active LearningObject	—Unverified	0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning	Dec 7, 2022	Active LearningEfficient Exploration	—Unverified	0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation	Dec 7, 2022	Active LearningModel Selection	—Unverified	0
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection	Dec 6, 2022	Active LearningActive Object Detection	CodeCode Available	1
Dissimilar Nodes Improve Graph Active Learning	Dec 5, 2022	Active LearningGraph Embedding	CodeCode Available	0
Active learning using adaptable task-based prioritisation	Dec 3, 2022	Active LearningMeta Reinforcement Learning	—Unverified	0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction	Dec 2, 2022	Active LearningGraph Learning	—Unverified	0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images	Dec 2, 2022	Active LearningClustering	—Unverified	0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data	Dec 2, 2022	Active LearningGenerative Adversarial Network	—Unverified	0

Show:10 25 50

← PrevPage 106 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