Learning about Interpretable Machine Learning Methods.
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Updated
Mar 13, 2021 - Jupyter Notebook
Learning about Interpretable Machine Learning Methods.
Decompose Thermo Gravimetrical Analysis (TGA) curves into simpler logistic curves representing mass-change events with a chemical interpretation. All of the analysis is performed with the TensorFlow library for the creation of a NN-analogous model and optimization.
Pytorch example of path-explain using Pytorch
Course project for CS726 @ IIT Bombay.
A curated list of awesome machine learning interpretability resources.
Predicting categories of scientific papers with advanced machine learning techniques involving class imbalance in multi-label data and explainable machine learning.
Supplementary programmes for DeRDaVa: Deletion-Robust Data Valuation for Machine Learning.
Investigating Machine Learning explainability in credit risk models by utilising LIME and DiCE methods
JAX-based Model Explanation and Interpretation Library
How to enhance the interpretability of powerful black-box models?
Tasks for Advanced Machine Learning Technologies Course @ ITMO University.
Notebooks for "Interpretable Machine Learning" course at University of Warsaw, 2021. Each homework utilises different XAI and ML techniques on different data sets.
Neural model interpretation on MRI data
Creating the model and approach to manage and adjust the process/equipment
Python implementation of Causal Rule Ensemble algorithm.
A radiomic interpretation tool based on Shapley values
seqgra: Synthetic rule-based biological sequence data generation for architecture evaluation and search
Exploring interpretable machine learning
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