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 201–225 of 3073 papers

Title	Date	Tasks	Status	Hype
Model-Change Active Learning in Graph-Based Semi-Supervised Learning	Oct 14, 2021	Active Learning	CodeCode Available	1
Class-Balanced Active Learning for Image Classification	Oct 9, 2021	Active LearningClassification	CodeCode Available	1
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum	Oct 7, 2021	Active Learning	CodeCode Available	1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)	Oct 6, 2021	Active Learning	CodeCode Available	1
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning	Oct 6, 2021	Active LearningSemi-Supervised Image Classification (Cold Start)	CodeCode Available	1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning	Sep 11, 2021	Active LearningMissing Labels	CodeCode Available	1
Cartography Active Learning	Sep 9, 2021	Active Learningtext-classification	CodeCode Available	1
Active Learning by Acquiring Contrastive Examples	Sep 8, 2021	Active LearningDiversity	CodeCode Available	1
Fluent: An AI Augmented Writing Tool for People who Stutter	Aug 23, 2021	Active Learning	CodeCode Available	1
Influence Selection for Active Learning	Aug 20, 2021	Active LearningDiversity	CodeCode Available	1
Multi-Anchor Active Domain Adaptation for Semantic Segmentation	Aug 18, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Semi-Supervised Active Learning with Temporal Output Discrepancy	Jul 29, 2021	Active Learningimage-classification	CodeCode Available	1
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation	Jul 25, 2021	Active LearningDeep Learning	CodeCode Available	1
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers	Jul 12, 2021	Active LearningClassification	CodeCode Available	1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering	Jul 6, 2021	Active LearningObject Recognition	CodeCode Available	1
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios	Jul 1, 2021	Active Learningimage-classification	CodeCode Available	1
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks	Jun 25, 2021	Active LearningBoundary Detection	CodeCode Available	1
TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration	Jun 24, 2021	Active LearningDocument Classification	CodeCode Available	1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning	Jun 22, 2021	Active Learning	CodeCode Available	1
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation	Jun 20, 2021	Active LearningImage Segmentation	CodeCode Available	1
On Minimizing Cost in Legal Document Review Workflows	Jun 18, 2021	Active LearningTAR	CodeCode Available	1
Gone Fishing: Neural Active Learning with Fisher Embeddings	Jun 17, 2021	Active Learning	CodeCode Available	1
Visual Transformer for Task-aware Active Learning	Jun 7, 2021	Active Learning	CodeCode Available	1
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design	Jun 7, 2021	Active LearningEvolutionary Algorithms	CodeCode Available	1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains	May 23, 2021	Active LearningBayesian Optimisation	CodeCode Available	1

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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