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 2581–2590 of 3073 papers

Title	Date	Tasks	Status
A Practical Incremental Learning Framework For Sparse Entity Extraction	Jun 26, 2018	Active LearningEntity Extraction using GAN	CodeCode Available
A Machine-learning framework for automatic reference-free quality assessment in MRI	Jun 25, 2018	Active LearningBIG-bench Machine Learning	—Unverified
On the Relationship between Data Efficiency and Error for Uncertainty Sampling	Jun 15, 2018	Active Learningregression	CodeCode Available
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network	Jun 14, 2018	Active LearningGeneral Classification	—Unverified
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning	Jun 12, 2018	Active LearningDeep Reinforcement Learning	—Unverified
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource	Jun 11, 2018	Active LearningCross-Lingual Transfer	CodeCode Available
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data	Jun 7, 2018	Active LearningGeneral Classification	—Unverified
Model-based active learning to detect isometric deformable objects in the wild with deep architectures	Jun 7, 2018	Active LearningObject	—Unverified
Probabilistic Model-Agnostic Meta-Learning	Jun 7, 2018	Active LearningFew-Shot Image Classification	—Unverified
Finding Convincing Arguments Using Scalable Bayesian Preference Learning	Jun 6, 2018	Active LearningVariational Inference	CodeCode Available

Show:10 25 50

← PrevPage 259 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