| TTHRESH: Tensor compression for multidimensional visual data R Ballester-Ripoll, P Lindstrom, R Pajarola IEEE Transactions on Visualization and Computer Graphics 26 (9), 2891-2903, 2019 | 124 | 2019 |
| A simulated annealing approach for the joint order batching and order picker routing problem with weight restrictions EH Grosse, CH Glock, R Ballester-Ripoll International Journal of Operations and Quantitative Management 20 (2), 65-83, 2014 | 69 | 2014 |
| Sobol tensor trains for global sensitivity analysis R Ballester-Ripoll, EG Paredes, R Pajarola Reliability Engineering & System Safety 183, 311-322, 2019 | 57 | 2019 |
| Period selection for minimal hyperperiod in periodic task systems I Ripoll, R Ballester-Ripoll IEEE Transactions on Computers 62 (9), 1813-1822, 2012 | 51 | 2012 |
| Lossy volume compression using Tucker truncation and thresholding R Ballester-Ripoll, R Pajarola The Visual Computer 32, 1433-1446, 2016 | 47 | 2016 |
| Task period selection to minimize hyperperiod V Brocal, P Balbastre, R Ballester, I Ripoll IEEE 16th Conference on Emerging Technologies & Factory Automation, 1-4, 2011 | 31 | 2011 |
| VIAN: A visual annotation tool for film analysis G Halter, R Ballester‐Ripoll, B Flueckiger, R Pajarola Computer Graphics Forum 38 (3), 119-129, 2019 | 30 | 2019 |
| Analysis of tensor approximation for compression-domain volume visualization R Ballester-Ripoll, SK Suter, R Pajarola Computers & Graphics 47, 34-47, 2015 | 30 | 2015 |
| Morphoproteomic Characterization of Lung Squamous Cell Carcinoma Fragmentation, a Histological Marker of Increased Tumor Invasiveness R Casanova, D Xia, U Rulle, P Nanni, J Grossmann, B Vrugt, R Wettstein, ... Cancer Research 77 (10), 2585-2593, 2017 | 17 | 2017 |
| A surrogate visualization model using the tensor train format R Ballester-Ripoll, EG Paredes, R Pajarola SIGGRAPH ASIA 2016 Symposium on Visualization, 1-8, 2016 | 11 | 2016 |
| Multiresolution volume filtering in the tensor compressed domain R Ballester-Ripoll, D Steiner, R Pajarola IEEE transactions on visualization and computer graphics 24 (10), 2714-2727, 2017 | 10 | 2017 |
| Computing Sobol indices in probabilistic graphical models R Ballester-Ripoll, M Leonelli Reliability Engineering & System Safety 225, 108573, 2022 | 9 | 2022 |
| Tensor algorithms for advanced sensitivity metrics R Ballester-Ripoll, EG Paredes, R Pajarola SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1172-1197, 2018 | 9 | 2018 |
| Deep learning tools for foreground-aware analysis of film colors B Flueckiger, N Evirgen, EG Paredes, R Ballester-Ripoll, R Pajarola AVinDH SIG, 2017 | 7 | 2017 |
| You only derive once (YODO): automatic differentiation for efficient sensitivity analysis in Bayesian networks R Ballester-Ripoll, M Leonelli International Conference on Probabilistic Graphical Models, 169-180, 2022 | 6 | 2022 |
| tntorch: Tensor network learning with PyTorch M Usvyatsov, R Ballester-Ripoll, K Schindler The Journal of Machine Learning Research 23 (1), 9394-9399, 2022 | 6 | 2022 |
| Are quantum computers practical yet? a case for feature selection in recommender systems using tensor networks A Nikitin, A Chertkov, R Ballester-Ripoll, I Oseledets, E Frolov arXiv preprint arXiv:2205.04490, 2022 | 5 | 2022 |
| Cherry-picking gradients: Learning low-rank embeddings of visual data via differentiable cross-approximation M Usvyatsov, A Makarova, R Ballester-Ripoll, M Rakhuba, A Krause, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 5 | 2021 |
| Tensor decompositions for integral histogram compression and look-up R Ballester-Ripoll, R Pajarola IEEE Transactions on Visualization and Computer Graphics 25 (2), 1435-1446, 2018 | 5 | 2018 |
| Compressing Bidirectional Texture Functions via Tensor Train Decomposition R Ballester-Ripoll, R Pajarola Pacific Graphics Short Papers, 2016 | 4 | 2016 |
