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 481–490 of 3073 papers

Title	Date	Tasks	Status	Score
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering	Sep 21, 2022	Active LearningClustering	CodeCode Available	5
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots	Jan 21, 2013	Active Learning	CodeCode Available	5
Active Decision Boundary Annotation with Deep Generative Models	Mar 20, 2017	Active Learning	CodeCode Available	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	5
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available	5
Active Learning for Abstractive Text Summarization	Jan 9, 2023	Abstractive Text SummarizationActive Learning	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Feedback Coding for Active Learning	Feb 28, 2021	Active Learning	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	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