CLOSED Call for Papers: Special Issue on Non-Euclidean Deep Learning
TPAMI seeks submissions for an upcoming special issue.
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Submissions Due: 15 September 2019
Paper submission due: CLOSED First Notification: 1 January 2020 Revision: 1 March 2020 Final Decision: 1 May 2020 Publication: August 2020 (tentative)
Aim and Scope
Over the past decade, deep learning has had a revolutionary impact on a broad range of fields, from computer vision to natural language processing and speech analysis. Deep learning technologies are estimated to have added billions in business value, created new markets, and transformed entire industrial segments. Most of today’s successful deep learning methods such as convolutional neural networks (CNNs) rely on classical signal processing models that limit their applicability to data with underlying Euclidean grid-like structure, e.g. images or acoustic signals. Yet, many applications deal with non-Euclidean (graph- or manifold-structured) data such as social networks in computational sociology, molecular graphs in chemistry, interactomes in system biology, and 3D point clouds in computer vision and graphics. Until recently, the lack of deep learning models capable of correctly dealing with non-Euclidean data has been a major obstacle in these fields.
This special issue addresses the need to bring together leading efforts in non-Euclidean deep learning across all communities. We seek theoretical, algorithmic, and methodological advances, as well as new applications and uses. In particular, we are interested in works on the theoretical foundations of non-Euclidean deep learning.
Topics and Guidelines
This special issue targets researchers and practitioners from both industry and academia to provide a forum in which to publish recent state-of-the-art achievements in Non-Euclidean Deep Learning. Topics of interest include, but are not limited to:
Theoretical aspects of non-Euclidean deep learning
Relaxation of NP-hard problems using graph deep learning
Generalized filters and pooling operators for non-Euclidean data
Generative models (such as auto-encoders and GANs) for graphs and manifolds.
Unsupervised learning on graphs
Adversarial attacks on graphs, and protection against adversarial attacks
Applications such as computer vision, graphics, shape analysis, biology, medical imaging, physics, computational social sciences, and complex network analysis
Deep learning on non-Euclidean domains is a common theme. This will be a criterion in evaluating submissions.
Michael Bronstein*, Imperial College London (UK), firstname.lastname@example.org
Joan Bruna, New York University (USA), email@example.com
Taco Cohen, Qualcomm AI Research (Netherlands), firstname.lastname@example.org
Marco Gori, University of Siena (Italy), email@example.com
Pietro Lio’, University of Cambridge (UK), firstname.lastname@example.org
Jure Leskovec, Stanford University (USA), email@example.com
Le Song, Georgia Institute of Technology (USA), firstname.lastname@example.org
Oriol Vinyals, DeepMind (UK), email@example.com
Stefanos Zafeiriou*, Imperial College London (UK), firstname.lastname@example.org