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Rafael Ballester-Ripoll
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2020 – today
- 2024
- [j16]Rafael Ballester-Ripoll
:
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions. SIAM/ASA J. Uncertain. Quantification 12(2): 289-308 (2024) - [j15]Rafael Ballester-Ripoll
High-dimensional scalar function visualization using principal parameterizations. Vis. Comput. 40(4): 2571-2588 (2024) - [i12]Rafael Ballester-Ripoll, Manuele Leonelli:
Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. CoRR abs/2406.05764 (2024) - 2023
- [j14]Rafael Ballester-Ripoll
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. Int. J. Approx. Reason. 159: 108929 (2023) - [i11]Rafael Ballester-Ripoll, Manuele Leonelli:
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. CoRR abs/2302.00364 (2023) - 2022
- [j13]Rafael Ballester-Ripoll
Tensor approximation of cooperative games and their semivalues. Int. J. Approx. Reason. 142: 94-108 (2022) - [j12]Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler:
tntorch: Tensor Network Learning with PyTorch. J. Mach. Learn. Res. 23: 208:1-208:6 (2022) - [j11]Rafael Ballester-Ripoll
Computing Sobol indices in probabilistic graphical models. Reliab. Eng. Syst. Saf. 225: 108573 (2022) - [c7]Mikhail Usvyatsov, Rafael Ballester, Lina Bashaeva, Konrad Schindler, Gonzalo Ferrer, Ivan V. Oseledets:
T4DT: Tensorizing Time for Learning Temporal 3D Visual Data. BMVC 2022: 348 - [c6]Rafael Ballester-Ripoll
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. PGM 2022: 169-180 - [i10]Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan V. Oseledets, Evgeny Frolov:
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks. CoRR abs/2205.04490 (2022) - [i9]Rafael Ballester-Ripoll, Manuele Leonelli:
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. CoRR abs/2206.08687 (2022) - [i8]Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler:
tntorch: Tensor Network Learning with PyTorch. CoRR abs/2206.11128 (2022) - [i7]Mikhail Usvyatsov, Rafael Ballester-Ripoll, Lina Bashaeva, Konrad Schindler, Gonzalo Ferrer, Ivan V. Oseledets:
T4DT: Tensorizing Time for Learning Temporal 3D Visual Data. CoRR abs/2208.01421 (2022) - 2021
- [j10]Haiyan Yang
SenVis: Interactive Tensor-based Sensitivity Visualization. Comput. Graph. Forum 40(3): 275-286 (2021) - [c5]Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler:
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. ICCV 2021: 11406-11415 - [i6]Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler:
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. CoRR abs/2105.14250 (2021) - [i5]Rafael Ballester-Ripoll, Manuele Leonelli:
Global sensitivity analysis in probabilistic graphical models. CoRR abs/2110.03749 (2021) - 2020
- [j9]Rafael Ballester-Ripoll
TTHRESH: Tensor Compression for Multidimensional Visual Data. IEEE Trans. Vis. Comput. Graph. 26(9): 2891-2903 (2020)
2010 – 2019
- 2019
- [j8]Gaudenz Halter, Rafael Ballester-Ripoll
VIAN: A Visual Annotation Tool for Film Analysis. Comput. Graph. Forum 38(3): 119-129 (2019) - [j7]Rafael Ballester-Ripoll
Sobol tensor trains for global sensitivity analysis. Reliab. Eng. Syst. Saf. 183: 311-322 (2019) - [j6]Rafael Ballester-Ripoll
Tensor Decompositions for Integral Histogram Compression and Look-Up. IEEE Trans. Vis. Comput. Graph. 25(2): 1435-1446 (2019) - [c4]Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola:
Tensor Methods for Global Sensitivity Analysis. GI-Jahrestagung 2019: 275-276 - 2018
- [j5]Rafael Ballester-Ripoll
Tensor Algorithms for Advanced Sensitivity Metrics. SIAM/ASA J. Uncertain. Quantification 6(3): 1172-1197 (2018) - [j4]Rafael Ballester-Ripoll
Multiresolution Volume Filtering in the Tensor Compressed Domain. IEEE Trans. Vis. Comput. Graph. 24(10): 2714-2727 (2018) - [d1]Enrique G. Paredes, Rafael Ballester-Ripoll:
SGEMM GPU kernel performance. UCI Machine Learning Repository, 2018 - [i4]Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola:
TTHRESH: Tensor Compression for Multidimensional Visual Data. CoRR abs/1806.05952 (2018) - [i3]Rafael Ballester-Ripoll, Renato Pajarola:
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations. CoRR abs/1809.03618 (2018) - 2017
- [i2]Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola:
Sobol Tensor Trains for Global Sensitivity Analysis. CoRR abs/1712.00233 (2017) - [i1]Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola:
Tensor Approximation of Advanced Metrics for Sensitivity Analysis. CoRR abs/1712.01633 (2017) - 2016
- [j3]Rafael Ballester-Ripoll
Lossy volume compression using Tucker truncation and thresholding. Vis. Comput. 32(11): 1433-1446 (2016) - [c3]Rafael Ballester-Ripoll
Compressing Bidirectional Texture Functions via Tensor Train Decomposition. PG (Short Papers) 2016: 19-22 - [c2]Rafael Ballester-Ripoll
A surrogate visualization model using the tensor train format. SIGGRAPH Asia Symposium on Visualization 2016: 13:1-13:8 - 2015
- [j2]Rafael Ballester-Ripoll
Analysis of tensor approximation for compression-domain volume visualization. Comput. Graph. 47: 34-47 (2015) - 2013
- [j1]Ismael Ripoll
Period Selection for Minimal Hyperperiod in Periodic Task Systems. IEEE Trans. Computers 62(9): 1813-1822 (2013) - 2011
- [c1]Vicent Brocal, Patricia Balbastre
Task period selection to minimize hyperperiod. ETFA 2011: 1-4
Coauthor Index
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