Active Learning
748 papers with code • 1 benchmarks • 15 datasets
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
Benchmarks
These leaderboards are used to track progress in Active Learning
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Libraries
Use these libraries to find Active Learning models and implementationsDatasets
CIFAR-10
MNIST-8M
DeepWeeds
HJDataset
Latest papers with no code
Classification Tree-based Active Learning: A Wrapper Approach
A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions.
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Specifically, we propose a gradient-based approach to assess epistemic uncertainty, analyzing the gradients of outputs relative to model parameters, and thereby indicating necessary model adjustments to accurately represent the inputs.
Active Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system.
Interactive Ontology Matching with Cost-Efficient Learning
The creation of high-quality ontologies is crucial for data integration and knowledge-based reasoning, specifically in the context of the rising data economy.
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model.
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth
This paper details seminal work on defect segmentation pipeline using in-situ optical images to identify features that indicate defective states that are visible at the macroscale.
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth
This paper compares various traditional and machine learning-driven approaches for feature extraction in the diamond growth domain, proposing a novel deep learning-driven semantic segmentation approach to isolate and classify accurate pixel masks of geometric features like diamond, pocket holder, and background, along with their derivative features based on shape and size.
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
This approach aims to address both challenges by focusing on the medical imaging context and utilizing an inherently interpretable model based on prototypes.
Focused Active Learning for Histopathological Image Classification
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
While our implementation focused on the QM9 quantum-chemical dataset for a specific design task-finding molecules with a large dipole moment-our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.
