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 231–240 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models	Dec 16, 2021	Active LearningLanguage Modeling	CodeCode Available	1	5
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes	Dec 12, 2020	Active LearningBinary Classification	CodeCode Available	1	5
A Tutorial on Thompson Sampling	Jul 7, 2017	Active LearningProduct Recommendation	CodeCode Available	1	5
A Holistic Approach to Undesired Content Detection in the Real World	Aug 5, 2022	Active Learning	CodeCode Available	1	5
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition	Jan 8, 2020	Active Learningnamed-entity-recognition	CodeCode Available	1	5
Machine-learning-accelerated simulations to enable automatic surface reconstruction	May 12, 2023	Active LearningSurface Reconstruction	CodeCode Available	1	5
Making Better Use of Unlabelled Data in Bayesian Active Learning	Apr 26, 2024	Active Learning	CodeCode Available	1	5
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning	Oct 5, 2022	Active LearningContrastive Learning	CodeCode Available	1	5
Learning Loss for Active Learning	May 9, 2019	Active Learningimage-classification	CodeCode Available	1	5
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers	Jun 16, 2023	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