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 11–20 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network	Jun 11, 2024	3D Object DetectionActive Learning	CodeCode Available	2	5
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents	Feb 21, 2024	Active LearningPosition	CodeCode Available	2	5
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI	Jul 19, 2022	Active LearningAutoML	CodeCode Available	2	5
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations	Jun 23, 2022	Active LearningScheduling	CodeCode Available	2	5
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data	May 16, 2024	Active Learningscientific discovery	CodeCode Available	2	5
Active Generalized Category Discovery	Mar 7, 2024	Active Learningimbalanced classification	CodeCode Available	2	5
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks	May 16, 2023	8kActive Learning	CodeCode Available	2	5
Active Prompting with Chain-of-Thought for Large Language Models	Feb 23, 2023	Active LearningZero-Shot Learning	CodeCode Available	2	5
DCoM: Active Learning for All Learners	Jul 1, 2024	Active LearningAll	CodeCode Available	2	5
ALBench: A Framework for Evaluating Active Learning in Object Detection	Jul 27, 2022	Active Learningimage-classification	CodeCode Available	2	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