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 76–100 of 3073 papers

Title	Date	Tasks	Status	Hype
Open-CRB: Towards Open World Active Learning for 3D Object Detection	Oct 16, 2023	3D Object DetectionActive Learning	CodeCode Available	1
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design	Oct 5, 2023	Active LearningDrug Design	CodeCode Available	1
Towards Free Data Selection with General-Purpose Models	Sep 29, 2023	Active Learning	CodeCode Available	1
Explaining Predictive Uncertainty with Information Theoretic Shapley Values	Sep 21, 2023	Active LearningDecision Making Under Uncertainty	CodeCode Available	1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation	Sep 11, 2023	Active LearningDrug Discovery	CodeCode Available	1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation	Aug 22, 2023	Active LearningPoint Cloud Segmentation	CodeCode Available	1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation	Jul 22, 2023	Active LearningImage Segmentation	CodeCode Available	1
Divide and Adapt: Active Domain Adaptation via Customized Learning	Jul 21, 2023	Active LearningDomain Adaptation	CodeCode Available	1
ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models	Jul 1, 2023	Active LearningModel Selection	CodeCode Available	1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks	Jun 21, 2023	Active LearningBrain Tumor Segmentation	CodeCode Available	1
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning	Jun 16, 2023	Active LearningBenchmarking	CodeCode Available	1
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers	Jun 16, 2023	Active Learning	CodeCode Available	1
Towards Balanced Active Learning for Multimodal Classification	Jun 14, 2023	Active LearningClassification	CodeCode Available	1
Memory-Based Dual Gaussian Processes for Sequential Learning	Jun 6, 2023	Active LearningBayesian Optimization	CodeCode Available	1
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information	May 30, 2023	Active LearningInformativeness	CodeCode Available	1
Parallelized Acquisition for Active Learning using Monte Carlo Sampling	May 30, 2023	Active LearningBayesian Inference	CodeCode Available	1
Label-Efficient Learning in Agriculture: A Comprehensive Review	May 24, 2023	Active LearningPlant Phenotyping	CodeCode Available	1
Machine-learning-accelerated simulations to enable automatic surface reconstruction	May 12, 2023	Active LearningSurface Reconstruction	CodeCode Available	1
Disentangled Multi-Fidelity Deep Bayesian Active Learning	May 7, 2023	Active LearningGaussian Processes	CodeCode Available	1
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation	Apr 23, 2023	3D Semantic SegmentationActive Learning	CodeCode Available	1
Prediction-Oriented Bayesian Active Learning	Apr 17, 2023	Active LearningPrediction	CodeCode Available	1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments	Apr 5, 2023	Active LearningRecommendation Systems	CodeCode Available	1
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks	Apr 3, 2023	Active LearningQuestion Answering	CodeCode Available	1
AISecKG: Knowledge Graph Dataset for Cybersecurity Education	Mar 30, 2023	Active LearningKnowledge Graphs	CodeCode Available	1
Adaptive Superpixel for Active Learning in Semantic Segmentation	Mar 29, 2023	Active LearningSegmentation	CodeCode Available	1

Show:10 25 50

← PrevPage 4 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