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Title: GitHub - orionr/captum: Model interpretability and understanding for PyTorch

Open Graph Title: GitHub - orionr/captum: Model interpretability and understanding for PyTorch

X Title: GitHub - orionr/captum: Model interpretability and understanding for PyTorch

Description: Model interpretability and understanding for PyTorch - orionr/captum

Open Graph Description: Model interpretability and understanding for PyTorch - orionr/captum

X Description: Model interpretability and understanding for PyTorch - orionr/captum

Opengraph URL: https://github.com/orionr/captum

X: @github

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https://patch-diff.githubusercontent.com/orionr/captum/blob/master/website/static/img/captum_logo.png
https://anaconda.org/pytorch/captum
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https://patch-diff.githubusercontent.com/orionr/captum#references-for-algorithms-mentioned-here
Axiomatic Attribution for Deep Networks, Mukund Sundararajan et al. 2017https://arxiv.org/abs/1703.01365
Did the Model Understand the Question?, Pramod K. Mudrakarta, et al. 2018https://arxiv.org/abs/1805.05492
Investigating the influence of noise and distractors on the interpretation of neural networks, Pieter-Jan Kindermans et al. 2016https://arxiv.org/abs/1611.07270
SmoothGrad: removing noise by adding noise, Daniel Smilkov et al. 2017https://arxiv.org/abs/1706.03825
Sanity Checks for Saliency Maps, Julius Adebayo et al. 2018https://arxiv.org/abs/1810.03292
How Important is a neuron?, Kedar Dhamdhere et al. 2018https://arxiv.org/abs/1805.12233
Computationally Efficient Measures of Internal Neuron Importance, Avanti Shrikumar et al. 2018https://arxiv.org/pdf/1807.09946.pdf
Learning Important Features Through Propagating Activation Differences, Avanti Shrikumar et al. 2017https://arxiv.org/pdf/1704.02685.pdf
Towards better understanding of gradient-based attribution methods for deep neural networks, Marco Ancona et al. 2018https://openreview.net/pdf?id=Sy21R9JAW
Computationally Efficient Measures of Internal Neuron Importance, Avanti Shrikumar et al. 2018https://arxiv.org/pdf/1807.09946.pdf
A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg et al. 2017http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions
Influence-Directed Explanations for Deep Convolutional Networks, Klas Leino et al. 2018https://arxiv.org/pdf/1802.03788.pdf
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, K. Simonyan, et. al. 2014https://arxiv.org/pdf/1312.6034.pdf
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, Ramprasaath R. Selvaraju et al. 2017https://arxiv.org/abs/1610.02391.pdf
Visualizing and Understanding Convolutional Networks, Matthew D Zeiler et al. 2014https://arxiv.org/pdf/1311.2901.pdf
Striving for Simplicity: The All Convolutional Net, Jost Tobias Springenberg et al. 2015https://arxiv.org/pdf/1412.6806.pdf
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