Code for paper: Noisy Batch Active Learning with Deterministic Annealing
-
Updated
Oct 30, 2020 - Jupyter Notebook
Code for paper: Noisy Batch Active Learning with Deterministic Annealing
Stratification of multi-label datasets
Hierarchical Uncertainty Aggregation (Park et al., 2022) in Rust.
Undergraduate Research of Department of Electrical Engineering, NTHU
Repository to simulate and compare different classical active learning methods.
OBEBS method
Proposed assignment notebooks for Advanced Topics in Machine Learning tasks
Active learning; Query by committee; Ensemble averaging; Committee machines; Neural Network Potentials
Files and works related to the Machine Learning Course
Driver recognition and Analysis System, that consists of: active learning module for reduction of annotated images required and training time, a motion blur detection module for identification and localization of blur to retake an image in case of blurry images, and an open set recognition module to reduce false positives and increase accuracy.
Toolset for active learning based control of medical free-text annotations
An interactive, visual workflow of active learning using the MNIST dataset.
An interactive data exploration software based on active learning.
Using Active learning from scratch in a simple prediction task with Julia language
CATI is a platform assisting end-users in the construction of an annotated corpus. It combines event-detection with Active Learning. This platform is supported by LABEX IMU under the project IDENUM: Identités numériques urbaines.
Add a description, image, and links to the active-learning topic page so that developers can more easily learn about it.
To associate your repository with the active-learning topic, visit your repo's landing page and select "manage topics."