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 1831–1840 of 3073 papers

Title	Date	Tasks	Status
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation	Jan 22, 2021	Active LearningEnsemble Learning	CodeCode Available
Active Hybrid Classification	Jan 21, 2021	Active LearningClassification	—Unverified
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates	Jan 20, 2021	Active LearningTransfer Learning	—Unverified
Autonomous synthesis of metastable materials	Jan 19, 2021	Active LearningEfficient Exploration	—Unverified
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach	Jan 18, 2021	Active LearningBenchmarking	—Unverified
Diverse Complexity Measures for Dataset Curation in Self-driving	Jan 16, 2021	Active LearningMotion Forecasting	—Unverified
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing	Jan 15, 2021	Active LearningDiversity	—Unverified
Improved active output selection strategy for noisy environments	Jan 10, 2021	Active LearningGaussian Processes	—Unverified
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions	Jan 10, 2021	Active Learning	—Unverified
Deep Diffusion Processes for Active Learning of Hyperspectral Images	Jan 8, 2021	Active LearningDeep Learning	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