| Compression and interpretability of deep neural networks via tucker tensor layer: From first principles to tensor valued back-propagation GG Calvi, A Moniri, M Mahfouz, Q Zhao, DP Mandic arXiv preprint arXiv:1903.06133, 2019 | 45* | 2019 |
| Support tensor machine for financial forecasting GG Calvi, V Lucic, DP Mandic ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 22 | 2019 |
| Tensor networks for latent variable analysis: Higher order canonical polyadic decomposition AH Phan, A Cichocki, I Oseledets, GG Calvi, S Ahmadi-Asl, DP Mandic IEEE transactions on neural networks and learning systems 31 (6), 2174-2188, 2019 | 21 | 2019 |
| Tensor-train recurrent neural networks for interpretable multi-way financial forecasting YL Xu, GG Calvi, DP Mandic 2021 International Joint Conference on Neural Networks (IJCNN), 1-5, 2021 | 15 | 2021 |
| HOTTBOX: Higher order tensor ToolBOX I Kisil, GG Calvi, BS Dees, DP Mandic arXiv preprint arXiv:2111.15662, 2021 | 13 | 2021 |
| Common and individual feature extraction using tensor decompositions: A remedy for the curse of dimensionality? I Kisil, GG Calvi, A Cichocki, DP Mandic 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 12 | 2018 |
| Tensor decompositions and practical applications: A hands-on tutorial I Kisil, GG Calvi, B Scalzo Dees, DP Mandic Recent Trends in Learning From Data: Tutorials from the INNS Big Data and …, 2020 | 11 | 2020 |
| Accelerating tensor contraction products via tensor-train decomposition [tips & tricks] I Kisil, GG Calvi, K Konstantinidis, YL Xu, DP Mandic IEEE Signal Processing Magazine 39 (5), 63-70, 2022 | 6 | 2022 |
| Tensor valued common and individual feature extraction: Multi-dimensional perspective I Kisil, GG Calvi, DP Mandic arXiv preprint arXiv:1711.00487, 2017 | 5 | 2017 |
| Reducing computational complexity of tensor contractions via tensor-train networks I Kisil, GG Calvi, K Konstantinidis, YL Xu, DP Mandic arXiv preprint arXiv:2109.00626, 2021 | 4 | 2021 |
| Feature fusion via tensor network summation GG Calvi, I Kisil, DP Mandic 2018 26th European Signal Processing Conference (EUSIPCO), 2623-2627, 2018 | 4 | 2018 |
| Tight lower bound on the tensor rank based on the maximally square unfolding GG Calvi, BS Dees, DP Mandic arXiv preprint arXiv:1909.05831, 2019 | 1 | 2019 |
| Text Mining–A Key Lynchpin in the Investment Process: A Survey A Konstantinidis, B Scalzo Dees, GG Calvi, DP Mandic Applications of Intelligent Systems, 181-193, 2018 | 1 | 2018 |
| Modelling economic stress through financial systemic balance index AC García, T Chanwimalueang, GG Calvi, A Hemakom, RM Ricós, ... 2016 IEEE International Conference on Digital Signal Processing (DSP), 565-569, 2016 | 1 | 2016 |
| A lower bound on the tensor rank based on its maximally square matrix unfolding GG Calvi, BS Dees, DP Mandic Signal Processing 180, 107862, 2021 | | 2021 |
| Higher order tensor decompositions for machine intelligence GG Calvi Imperial College London, 2021 | | 2021 |
| The sum of tensor networks GG Calvi, I Kisil, DP Mandic arXiv preprint arXiv:1711.00701, 2017 | | 2017 |
