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 881–890 of 3073 papers

Title	Date	Tasks	Status	Hype
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks	May 16, 2023	8kActive Learning	CodeCode Available	2
On Dataset Transferability in Active Learning for Transformers	May 16, 2023	Active Learningtext-classification	CodeCode Available	0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation	May 13, 2023	Active LearningBrain Tumor Segmentation	—Unverified	0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems	May 13, 2023	Active Learning	—Unverified	0
Machine-learning-accelerated simulations to enable automatic surface reconstruction	May 12, 2023	Active LearningSurface Reconstruction	CodeCode Available	1
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model	May 11, 2023	Active LearningBayesian Inference	—Unverified	0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach	May 11, 2023	Active LearningDecision Making	—Unverified	0
Accelerating Batch Active Learning Using Continual Learning Techniques	May 10, 2023	Active LearningContinual Learning	—Unverified	0
Disentangled Multi-Fidelity Deep Bayesian Active Learning	May 7, 2023	Active LearningGaussian Processes	CodeCode Available	1
Actively Discovering New Slots for Task-oriented Conversation	May 6, 2023	Active LearningConversational Search	CodeCode Available	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