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 3021–3030 of 3073 papers

Title	Date	Tasks	Status
Automated discovery of a robust interatomic potential for aluminum	Mar 10, 2020	Active Learning	CodeCode Available
Generation Of Colors using Bidirectional Long Short Term Memory Networks	Nov 11, 2023	Active LearningText-to-Image Generation	CodeCode Available
Generative Active Learning for Image Synthesis Personalization	Mar 22, 2024	Active LearningImage Generation	CodeCode Available
AutoAL: Automated Active Learning with Differentiable Query Strategy Search	Oct 17, 2024	Active Learning	CodeCode Available
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space	Nov 18, 2024	Active Learning	CodeCode Available
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation	Dec 1, 2018	Active LearningBayesian Optimisation	CodeCode Available
Overcoming Overconfidence for Active Learning	Aug 21, 2023	Active Learning	CodeCode Available
Generative Adversarial Active Learning for Unsupervised Outlier Detection	Sep 28, 2018	Active LearningBinary Classification	CodeCode Available
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP	Aug 1, 2023	Active Learning	CodeCode Available
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models	Mar 10, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available

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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