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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 2631–2640 of 3073 papers

Title	Date	Tasks	Status
CFlowNets: Continuous Control with Generative Flow Networks	Mar 4, 2023	Active Learningcontinuous-control	CodeCode Available
CAMAL: Optimizing LSM-trees via Active Learning	Sep 23, 2024	Active Learning	CodeCode Available
Pool-Based Sequential Active Learning for Regression	May 12, 2018	Active LearningDiversity	CodeCode Available
Active Learning for Top-K Rank Aggregation from Noisy Comparisons	Aug 1, 2017	Active Learning	CodeCode Available
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning	Feb 27, 2019	Active LearningGaussian Processes	CodeCode Available
Combining Data Generation and Active Learning for Low-Resource Question Answering	Nov 27, 2022	Active LearningAnswer Generation	CodeCode Available
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning	Jan 16, 2024	16kActive Learning	CodeCode Available
Multi-Domain Active Learning: Literature Review and Comparative Study	Jun 25, 2021	Active Learning	CodeCode Available
Deeply Supervised Active Learning for Finger Bones Segmentation	May 7, 2020	Active LearningIncremental Learning	CodeCode Available
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari	Sep 30, 2021	Active LearningAtari Games	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