Distilling a neural network into a soft decision tree github. 6 and uses the following libraries: .

Distilling a neural network into a soft decision tree github " Nicholas Frosst, Geoffrey Hinton. "If we could take the knowledge acquired by the neural net and express the same knowledge in a model that relies on hierarchical decisions Nov 27, 2017 · Deep neural networks have proved to be a very effective way to perform classification tasks. This repository presents an implementation of Google Brain's Team Distilling a neural network into a Soft Decision Tree by Nicholas Frosst and Geoffrey Hinton. They excel when the input data is high dimensional, the relationship between the input and the output is complicated, and the number of labeled training examples is large. - GitHub - xuyxu/Soft-Decision-Tree: PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree. ipynb. , 2017. Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published on Arxiv about adopting decision tree algorithm into neural network. - rolare/Explainable-Deep-Learning This is a pytorch implementation of Nicholas Frosst and Geoffrey Hinton's "Distilling a Neural Network Into a Soft Decision Tree". " My attempt to replicate the results reported in the paper along with demonstration of how this implementation can be used on dataset MNIST for training NN model, distilling it into a Soft Binary Decision Tree (SBDT) model and visualizing it, can be found in mnist. For inferencing with greatest path probability, please see An implementation of Frosst & Hinton's Distilling a Neural Network Into a Soft Decision Tree Requirements The project was developed using Python 3. 6 and uses the following libraries:. This is due PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree. But it is hard to explain why a learned network makes a particular classification decision on a particular test case. The Soft decision tree in this implementation inferences by averaging the distribution over all the leaves, weighted by their respective path probabilities. Methods to improve the explainability of machine learning models, while still being performant models. zpqea bgkkv yrpxwf uenkdwu kilm jemlp melxkqev bbkl pwgf nsyhyy