Explainable Neural Networks based on Additive Index Models | TDLS

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=J75cQmL7amA


Duration: 1:34:47
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13

Toronto Deep Learning Series, 23 July 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-07-23/

Paper Review: https://arxiv.org/abs/1806.01933

Speaker: https://www.linkedin.com/in/hassan-omidi-firouzi-4b423337/
Organizer: https://www.linkedin.com/in/amirfz/

Host: http://rbc.com/futuremakers/

Paper abstract:
"Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples."



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Tags:
deep learning
machine learning
interpretablility
explainability
interpretability
neural networks