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Limitations of Simple Feature Attribution Methods
We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.
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Limitations of Simple Feature Attribution Methods
https://deep.ghost.io/simple-feature-attribution
We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.
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https://deep.ghost.io/simple-feature-attributionLimitations of Simple Feature Attribution Methods
We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.
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8- og:site_nameAlta Cognita
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- og:titleLimitations of Simple Feature Attribution Methods
- og:descriptionWe examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.
- og:urlhttps://www.altacognita.com/simple-feature-attribution/
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- twitter:titleLimitations of Simple Feature Attribution Methods
- twitter:descriptionWe examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.
- twitter:urlhttps://www.altacognita.com/simple-feature-attribution/
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15- http://arxiv.org/abs/1311.2901?ref=altacognita.com
- http://arxiv.org/abs/1611.02639?ref=altacognita.com
- http://arxiv.org/abs/1703.01365?ref=altacognita.com
- http://arxiv.org/abs/1704.02685?ref=altacognita.com
- http://arxiv.org/abs/1705.05598?ref=altacognita.com