3 citations · 9 across the 7 of their papers we have counts for
7 papers
Understanding the Rank of Tensor Networks via an Intuitive Example-Driven Approach
Wuyang Zhou, Giorgos Iacovides, Kriton Konstantinidis +2
Tensor Network (TN) decompositions have emerged as an indispensable tool in Big Data analytics owing to their ability to provide compact low-rank representations, thus alleviating…
Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-expl…
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge…
Reducing Computational Complexity of Tensor Contractions via Tensor-Train Networks
Ilya Kisil, Giuseppe G. Calvi, Kriton Konstantinidis +2
There is a significant expansion in both volume and range of applications along with the concomitant increase in the variety of data sources. These ever-expanding trends have highl…
Tensor Networks for Multi-Modal Non-Euclidean Data
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Modern data sources are typically of large scale and multi-modal natures, and acquired on irregular domains, which poses serious challenges to traditional deep learning models. The…
Multi-Graph Tensor Networks
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized…
Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach
Alexandros Haliassos, Kriton Konstantinidis, Danilo P. Mandic
Efficient modelling of feature interactions underpins supervised learning for non-sequential tasks, characterized by a lack of inherent ordering of features (variables). The brute…