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NIPS IEVDL 2017 : NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning - Now what ?

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Link: http://www.interpretable-ml.org/nips2017workshop/
 
When Dec 9, 2017 - Dec 9, 2017
Where Long Beach, CA
Submission Deadline Nov 1, 2017
Notification Due Nov 8, 2017
Categories    deep learning   neural networks   interpretability   machine learning
 

Call For Papers

************ Call for Papers: NIPS IEVDL 2017 **************
The NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning - Now what ? aims to review recent techniques and establish new theoretical foundations for interpreting and understanding deep learning models. However, it will not stop at the methodological level, but also address the "now what?" question, where we aim to take the next step by exploring and extending practical usefulness. The workshop will have speakers from various application domains (computer vision, NLP, neuroscience, medicine), it will provide an opportunity for participants to learn from each other and initiate new interdisciplinary collaborations.

***** Important Dates *****
Submission deadline: 01 November, 2017
Author notification: 08 November, 2017
Workshop: 09 December, 2017

***** List of Speakers *****
Dhruv Batra (Georgia Tech)
Rich Caruana (Microsoft)
Trevor Darrell (UC Berkeley)
Marcel van Gerven (Radboud University)
Sepp Hochreiter (University Linz)
Been Kim (Google Brain)
Honglak Lee (University of Michigan)
Anh Nguyen (Auburn University)

***** Paper Submission *****
We call for papers on the following topics:
- interpretability of deep neural networks
- analysis and comparison of state-of-the-art models
- formalization of the interpretability problem
- interpretability for making ML socially acceptable
- applications of interpretability.

Submissions are required to stick to the NIPS format. Papers are limited to eight pages (excluding references) and will go through a review process. A selection of accepted papers together with the invited contributions will be part of an edited book at Springer LNCS.

Submission website: https://cmt3.research.microsoft.com/IEVDL2017

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