Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

Testing for causality: a personal viewpoint

Clive WJ Granger · 1980

Earlier work this paper cites.

The central role of the propensity score in observational studies for causal effects

Paul R Rosenbaum and Donald B Rubin · 1983

Earlier work this paper cites.

Histograms of oriented gradients for human detection

Navneet Dalal · 2005

Earlier work this paper cites.

Making things happen: A theory of causal explanation

James Woodward · 2005

Earlier work this paper cites.

Visualizing higher-layer features of a deep network

Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009

Earlier work this paper cites.

Causality

Judea Pearl · 2009

Earlier work this paper cites.

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P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010

Earlier work this paper cites.

Deep inside convolutional networks: Visualising image classification models and saliency maps

Original

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013

Earlier work this paper cites.

Explaining and harnessing adversarial examples

Original

Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014

Earlier work this paper cites.

Visualizing and understanding convolutional networks

Original

Matthew D Zeiler and Rob Fergus · 2014

Cited alongside, same era.

On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015

Cited alongside, same era.

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Cited alongside, same era.

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Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015

Cited alongside, same era.

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Cited alongside, same era.

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David Silver, Aja Huang, Christopher J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016

Later among the works it cites.

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Richard Berk · 2017

Later among the works it cites.

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Stephanie Cuccaro-Alamin, Regan Foust, Rhema Vaithianathan, and Emily Putnam-Hornstein · 2017

Later among the works it cites.

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017

Later among the works it cites.

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