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 571–580 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning for Top-K Rank Aggregation from Noisy Comparisons	Aug 1, 2017	Active Learning	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
CFlowNets: Continuous Control with Generative Flow Networks	Mar 4, 2023	Active Learningcontinuous-control	CodeCode Available	5
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	CodeCode Available	5
CAMAL: Optimizing LSM-trees via Active Learning	Sep 23, 2024	Active Learning	CodeCode Available	5
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning	Jan 16, 2024	16kActive Learning	CodeCode Available	5
Buy Me That Look: An Approach for Recommending Similar Fashion Products	Aug 26, 2020	Active LearningArticles	CodeCode Available	5
Deep Bayesian Active Learning for Accelerating Stochastic Simulation	Jun 5, 2021	Active Learning	CodeCode Available	5
Active Learning for Semantic Segmentation with Multi-class Label Query	Sep 17, 2023	Active LearningMultiple Instance Learning	CodeCode Available	5
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation	Jan 29, 2024	Active LearningImage Segmentation	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