There are 8 repositories under interactive-machine-learning topic.
data-to-paper: Backward-traceable AI-driven scientific research
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Tornado is an open source Human-in-the-loop machine learning tool. It helps you label your dataset on the fly while training your model through a simple web user interface. It supports all data types: structured, text and image.
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
A system for building labeling tools
Tölvera is a library for exploring musical performance with artificial life (ALife) and self-organising systems.
Personalized Training for the Sequence Learning task with the NAO robot and the MUSE EEG sensor
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Interactive multimedia captioning with Keras
Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.
Paper list of Interactive Labeling Algorithm
RapidLib is a lightweight library for interactive machine learning. Bela is a platform for interactive sensor and audio processing.
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
Risk Factor Analysis for Medical Data, Open-source Machine Learning Platform
Sample Review & Feature Selection for Audio Datasets
Interactive feature selection web application
An R Shiny Application that let's users get inference from the data by the auto-generated visualizations and control, train and evaluate different ML models on the selected data.
Group project at Augsburg University
Interactive Machine Learning for ScratchX
Teach an ML model to read your gestures in your web browser
Implementation of "GrabCut": interactive foreground extraction using iterated graph cuts", in MATLAB
A two-way interactive app teaches user to draw and machine to learn
DrCaptcha is an interactive machine learning application. The purpose of the program is to the feedback provided by users, and to use it to optimize a machine learning model. The purpose of this model is to recognize handwritten letters and numbers.
Code for paper: "Coherent Hierarchical Multi-Label Classification Networks". Later modified in order to handle multiple explainations
Free amino acids in African indigenous vegetables: Analysis with improved HILIC-UHPLC-QqQ-MS/MS and interactive machine learning