data-to-paper: Backward-traceable AI-driven scientific research
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Updated
Sep 17, 2024 - Python
data-to-paper: Backward-traceable AI-driven scientific research
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.
An Interactive Machine Learning Toolkit
Tölvera is a library for exploring musical performance with artificial life (ALife) and self-organising systems.
Free amino acids in African indigenous vegetables: Analysis with improved HILIC-UHPLC-QqQ-MS/MS and interactive machine learning
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Teach an ML model to read your gestures in your web browser
Interactive Assessment Tool for Gaze-based Machine Learning Models in Information Retrieval
RapidLib is a lightweight library for interactive machine learning.
Code for paper: "Coherent Hierarchical Multi-Label Classification Networks". Later modified in order to handle multiple explainations
A system for building labeling tools
Interactive Neural Machine Translation tool
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.
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.
A two-way interactive app teaches user to draw and machine to learn
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
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.
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
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