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 791–800 of 3073 papers

Title	Date	Tasks	Status	Score
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning	Jan 16, 2024	16kActive Learning	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
Bayesian active learning for optimization and uncertainty quantification in protein docking	Jan 31, 2019	Active LearningBinary Classification	CodeCode Available	5
Bayesian Active Learning By Distribution Disagreement	Jan 2, 2025	Active Learningregression	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	Active Learning	CodeCode Available	5
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data	Feb 7, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	5
A Simple yet Brisk and Efficient Active Learning Platform for Text Classification	Jan 31, 2021	Active LearningBIG-bench Machine Learning	CodeCode Available	5
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Jun 19, 2019	Active Learning	CodeCode Available	5
Batch Decorrelation for Active Metric Learning	May 20, 2020	Active LearningDiversity	CodeCode Available	5
BatchGFN: Generative Flow Networks for Batch Active Learning	Jun 26, 2023	Active Learning	CodeCode Available	5

Show:10 25 50

← PrevPage 80 of 308Next →

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