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 2121–2130 of 3073 papers

Title	Date	Tasks	Status
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach	Jun 2, 2020	Active Learning	CodeCode Available
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples	Jun 1, 2020	Active LearningClassification	—Unverified
Adaptive quadrature schemes for Bayesian inference via active learning	May 31, 2020	Active LearningBayesian Inference	—Unverified
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning	May 30, 2020	Active Learningreinforcement-learning	CodeCode Available
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search	May 28, 2020	Active Learning	—Unverified
Active Fuzzing for Testing and Securing Cyber-Physical Systems	May 28, 2020	Active Learning	CodeCode Available
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification	May 28, 2020	Active LearningGeneral Classification	CodeCode Available
Minimizing Supervision in Multi-label Categorization	May 26, 2020	Active LearningGeneral Classification	—Unverified
Discriminative Active Learning for Domain Adaptation	May 24, 2020	Active LearningDiversity	—Unverified

Show:10 25 50

← PrevPage 213 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