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 2571–2580 of 3073 papers

Title	Date	Tasks	Status	Hype
Adversarial Distillation of Bayesian Neural Network Posteriors	Jun 27, 2018	Active LearningAnomaly Detection	CodeCode Available	0
Autonomous Wireless Systems with Artificial Intelligence	Jun 27, 2018	Active LearningManagement	—Unverified	0
A Practical Incremental Learning Framework For Sparse Entity Extraction	Jun 26, 2018	Active LearningEntity Extraction using GAN	CodeCode Available	0
Dropout-based Active Learning for Regression	Jun 26, 2018	Active Learningregression	—Unverified	0
A Machine-learning framework for automatic reference-free quality assessment in MRI	Jun 25, 2018	Active LearningBIG-bench Machine Learning	—Unverified	0
On the Relationship between Data Efficiency and Error for Uncertainty Sampling	Jun 15, 2018	Active Learningregression	CodeCode Available	0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network	Jun 14, 2018	Active LearningGeneral Classification	—Unverified	0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning	Jun 12, 2018	Active LearningDeep Reinforcement Learning	—Unverified	0
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource	Jun 11, 2018	Active LearningCross-Lingual Transfer	CodeCode Available	0
Bayesian Model-Agnostic Meta-Learning	Jun 11, 2018	Active Learningimage-classification	CodeCode Available	1

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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