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AAAI-19 workshop on Network Interpretability for Deep Learning

Network Interpretability for Deep Learning?

networkinterpretability.org

This workshop aims to bring together researchers, engineers, students in both academic and industrial communities who concern about the interpretability of deep learning models and, more importantly, the safety of applying these complex deep models in critical applications such as the medical diagnosis and the autonomous driving. Efforts along this direction are expected to open the black box of deep neural networks for better understanding and to build more transparent deep models which are interpretable to humans. Therefore, the main theme of the workshop is to build up consensus on the emerging topic of the network interpretability, by clarifying the motivation, the typical methodologies, the prospective trends, and the potential industrial applications of the network interpretability.

Topics include but are not limited to

● Theories of deep neural networks

● Visualization of neural networks

● Diagnosing and disentangling feature representations of neural networks

● Learning representations for neural networks which are interpretable, disentangled and/or compact

● Improving interpolation capacity of features for generative models.

● Probabilistic logic interpretation of deep learning

● Bridging feature representations between visual concepts and linguistic concepts.

● Safety and fairness of the deep learning models

● Industrial applications of interpretable deep neural networks

● Evaluation of the interpretability of neural networks

We are calling for extended abstracts with 2—4 pages to be showcased at a poster session along with short talk spotlights. We are also accepting full submissions with 6—8 pages which will not be included in the Proceedings of AAAI 2019, but we will at the option of the authors provide a link to the relevant arXiv submission.

Please submit workshop papers to networkinterpretability@gmail.com

Submission deadline: November 5, 2018

Notification date: November 26, 2018

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