Special Session: Tensor Decompositions in Deep Learning@ ESANN 2020

DESCRIPTION: In the latter years, tensor decompositions have been gaining increasing interest in the Big Data and Machine Learning community.  On the one hand, multiway data analysis provides powerful and efficient methods to address the processing of large scale and highly complex data, such as multivariate sensor signals. 

On the other hand, tensor decompositions have been shown to provide the necessary theoretical backbone to study the expressiveness properties of deep neural networks. More recently, tensors have started to find wide application in a variety of machine learning paradigms, ranging from neural networks to probabilistic models, to enable the efficient compression of the model parameters leveraging a wide range of decomposition methods from multilinear algebra. This special session aims to present the state­of­the­art on this increasingly relevant topic among ML theoretician and practitioners. To this end, we welcome both solid contributions and preliminary relevant results showing potential, limitations and challenges of new ideas related to the use of tensor decompositions in deep learning, neural networks, and machine learning at large.  Studies stemming from major research initiatives and projects focusing on the session topics are particularly welcome.

TOPICS OF INTEREST (non-exhaustive): – Tensor decompositions and multiway data analytic – Tensor decompositions for signal processing – Tensor neural networks – Tensor-based representation and manipulation of neural weights – Decomposing neural architectures through tensor manipulation – Theoretical analysis of neural networks through multiway algebra – Use of tensor decompositions in learning systems – Speeding up neural computations through tensor decompositions – Tensor based approaches for structured data processing (graphs, trees, sequences) – Tensors and dynamic memory (recurrent neural networks) – Applications of tensor neural networks to image and video processing – Applications of tensor neural networks to sensor and stream data analysis

SUBMISSION: Prospective authors must submit their paper through the ESANN portal following the instructions provided in www.esann.org. Each paper will undergo a peer reviewing process for its acceptance. Authors should send as soon as possible an e-mail with the tentative title of their contribution to the special session organisers.

IMPORTANT DATES:

Submission of papers: 18 November 2019

Notification of acceptance: 31 January 2020

ESANN conference: 22-24 April 2020

SPECIAL SESSION ORGANISERS:

Davide Bacciu, University of Pisa (Italy)

Danilo Mandic, Imperial College (UK)