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 221–230 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning	Jan 1, 2023	Active LearningMoment Retrieval	CodeCode Available	1	5
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy	Mar 15, 2022	Active LearningClassification	CodeCode Available	1	5
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1	5
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning	Jun 22, 2021	Active Learning	CodeCode Available	1	5
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design	Jun 7, 2021	Active LearningEvolutionary Algorithms	CodeCode Available	1	5
Knowledge-Aware Federated Active Learning with Non-IID Data	Nov 24, 2022	Active LearningFederated Learning	CodeCode Available	1	5
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning	Sep 11, 2021	Active LearningMissing Labels	CodeCode Available	1	5
Open Source Software for Efficient and Transparent Reviews	Jun 22, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1	5
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1	5
Bayesian active learning for production, a systematic study and a reusable library	Jun 17, 2020	Active Learning	CodeCode Available	1	5

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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