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 476–500 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning for Entity Filtering in Microblog Streams	Aug 1, 2015	Active Learning	CodeCode Available	5
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection	May 29, 2024	Active LearningModel Selection	CodeCode Available	5
Extracting Commonsense Properties from Embeddings with Limited Human Guidance	Jul 1, 2018	Active LearningCommon Sense Reasoning	CodeCode Available	5
Active Learning of Molecular Data for Task-Specific Objectives	Aug 20, 2024	Active LearningGaussian Processes	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
A Comparison of Strategies for Source-Free Domain Adaptation	May 1, 2022	Active LearningData Augmentation	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models	Apr 4, 2024	Active LearningDecision Making	CodeCode Available	5
Active Few-Shot Learning with FASL	Apr 20, 2022	Active LearningFew-Shot Learning	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	CodeCode Available	5
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots	Jan 21, 2013	Active Learning	CodeCode Available	5
Feedback Coding for Active Learning	Feb 28, 2021	Active Learning	CodeCode Available	5
Active Decision Boundary Annotation with Deep Generative Models	Mar 20, 2017	Active Learning	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
Few-Shot Learning with Graph Neural Networks	Nov 10, 2017	Active LearningCross-Domain Few-Shot	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Active Learning for Abstractive Text Summarization	Jan 9, 2023	Abstractive Text SummarizationActive Learning	CodeCode Available	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	5
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats	Sep 26, 2024	Active 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
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
Active Preference Learning for Ordering Items In- and Out-of-sample	May 5, 2024	Active Learning	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	5
Cooperative Inverse Reinforcement Learning	Jun 9, 2016	Active Learningreinforcement-learning	CodeCode Available	5

Show:10 25 50

← PrevPage 20 of 123Next →

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