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 731–740 of 3073 papers

Title	Date	Tasks	Status	Score
An Information Retrieval Approach to Building Datasets for Hate Speech Detection	Jun 17, 2021	Active LearningExplanation Generation	CodeCode Available	5
An Information-Theoretic Framework for Unifying Active Learning Problems	Dec 19, 2020	Active LearningBayesian Optimization	CodeCode Available	5
Active Labeling: Streaming Stochastic Gradients	May 26, 2022	Active Learningregression	CodeCode Available	5
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation	Nov 28, 2019	Active LearningBayesian Optimisation	CodeCode Available	5
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available	5
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models	Jun 3, 2023	Accented Speech RecognitionActive Learning	CodeCode Available	5
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification	Dec 13, 2020	Active LearningClustering	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	Active Learning	CodeCode Available	5
Active Learning for Abstractive Text Summarization	Jan 9, 2023	Abstractive Text SummarizationActive Learning	CodeCode Available	5
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available	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