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 126–150 of 3073 papers

Title	Date	Tasks	Status	Hype
Fine-Tuning Language Models via Epistemic Neural Networks	Nov 3, 2022	Active LearningLanguage Modeling	CodeCode Available	1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis	Oct 28, 2022	Active LearningAttribute	CodeCode Available	1
Provable Safe Reinforcement Learning with Binary Feedback	Oct 26, 2022	Active Learningreinforcement-learning	CodeCode Available	1
Multi-Objective GFlowNets	Oct 23, 2022	Active LearningDiversity	CodeCode Available	1
Bayesian Optimization with Conformal Prediction Sets	Oct 22, 2022	Active LearningBayesian Optimization	CodeCode Available	1
A Survey of Dataset Refinement for Problems in Computer Vision Datasets	Oct 21, 2022	Active Learning	CodeCode Available	1
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	CodeCode Available	1
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning	Oct 13, 2022	Active LearningInformativeness	CodeCode Available	1
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation	Oct 12, 2022	Active LearningHand Pose Estimation	CodeCode Available	1
Efficient Bayesian Updates for Deep Learning via Laplace Approximations	Oct 12, 2022	Active LearningDeep Learning	CodeCode Available	1
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning	Oct 5, 2022	Active LearningContrastive Learning	CodeCode Available	1
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
BenchPress: A Deep Active Benchmark Generator	Aug 13, 2022	Active LearningCPU	CodeCode Available	1
A Holistic Approach to Undesired Content Detection in the Real World	Aug 5, 2022	Active Learning	CodeCode Available	1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation	Aug 2, 2022	Active LearningSegmentation	CodeCode Available	1
Active Learning Strategies for Weakly-supervised Object Detection	Jul 25, 2022	Active LearningObject	CodeCode Available	1
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation	Jul 2, 2022	Active LearningBenchmarking	CodeCode Available	1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jul 1, 2022	Active Learningtext-classification	CodeCode Available	1
Deep Active Learning for Regression Using ε-weighted Hybrid Query Strategy	Jun 24, 2022	Active Learningregression	CodeCode Available	1
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning	Jun 22, 2022	Active LearningClassification	CodeCode Available	1
Active Bayesian Causal Inference	Jun 4, 2022	Active LearningCausal Discovery	CodeCode Available	1
PyRelationAL: a python library for active learning research and development	May 23, 2022	Active LearningBenchmarking	CodeCode Available	1
Active Learning Through a Covering Lens	May 23, 2022	Active LearningRepresentation Learning	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