[visionlist] AAAI-19 Workshop on Network Interpretability for Deep Learning

[Lixin Fan]

AAAI-19 Workshop on
Network Interpretability for Deep Learning

Overview
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
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 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

Submission
We are calling for extended abstracts with 2—4 pages and full submissions
with 6—8 pages. All the accepted papers will not be included in the
proceedings of AAAI 2019, but we will publish workshop proceedings on
arXiv.org.

Please submit workshop papers to networkinterpretability at gmail.com

Submission deadline: November 5 (extended to November 8), 2018

Notification date: November 26, 2018


Organizers:
Quanshi Zhang, SJTU, zqs1022 at sjtu.edu.cn
Lixin Fan， lixin.fan01 at gmail.com
Bolei Zhou, CUHK, bzhou at ie.cuhk.edu.hk

Please contact Quanshi Zhang if you have question.
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