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 271–280 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	CodeCode Available	1	5
Active Learning Through a Covering Lens	May 23, 2022	Active LearningRepresentation Learning	CodeCode Available	1	5
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis	Oct 28, 2022	Active LearningAttribute	CodeCode Available	1	5
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization	Jul 8, 2025	Active LearningAutomated Theorem Proving	CodeCode Available	1	5
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking	Dec 14, 2020	Active LearningContrastive Learning	CodeCode Available	1	5
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations	Dec 2, 2023	Active LearningGaussian Processes	CodeCode Available	1	5
Dataset Quantization with Active Learning based Adaptive Sampling	Jul 9, 2024	Active LearningDataset Distillation	CodeCode Available	1	5
Counting People by Estimating People Flows	Dec 1, 2020	Active LearningCrowd Counting	CodeCode Available	1	5
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1	5
Deep Active Learning for Regression Using ε-weighted Hybrid Query Strategy	Jun 24, 2022	Active Learningregression	CodeCode Available	1	5

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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