Rafael Ballester-Ripoll

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 31

Top Topics

Sensitivity Analysis

Top Venues

IEEE Trans. Vis. Comput. Graph.

Int. J. Approx. Reason.

SIAM/ASA J. Uncertain. Quantification

Publications

Rafael Ballester-Ripoll, Manuele Leonelli
Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. CoRR (2024)
Rafael Ballester-Ripoll
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions. SIAM/ASA J. Uncertain. Quantification 12 (2) (2024)
Rafael Ballester-Ripoll, Gaudenz Halter, Renato Pajarola
High-dimensional scalar function visualization using principal parameterizations. Vis. Comput. 40 (4) (2024)
Rafael Ballester-Ripoll, Manuele Leonelli
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. CoRR (2023)
Rafael Ballester-Ripoll, Manuele Leonelli
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. Int. J. Approx. Reason. 159 (2023)
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Lina Bashaeva, Konrad Schindler, Gonzalo Ferrer, Ivan V. Oseledets
T4DT: Tensorizing Time for Learning Temporal 3D Visual Data. CoRR (2022)
Rafael Ballester-Ripoll, Manuele Leonelli
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. PGM (2022)
Rafael Ballester-Ripoll, Manuele Leonelli
Computing Sobol indices in probabilistic graphical models. Reliab. Eng. Syst. Saf. 225 (2022)
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 (2022)
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
tntorch: Tensor Network Learning with PyTorch. CoRR (2022)
Rafael Ballester-Ripoll
Tensor approximation of cooperative games and their semivalues. Int. J. Approx. Reason. 142 (2022)
Rafael Ballester-Ripoll, Manuele Leonelli
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. CoRR (2022)
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
tntorch: Tensor Network Learning with PyTorch. J. Mach. Learn. Res. 23 (2022)
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim Rakhuba, Andreas Krause, Konrad Schindler
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. ICCV (2021)
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim Rakhuba, Andreas Krause, Konrad Schindler
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. CoRR (2021)
Haiyan Yang, Rafael Ballester-Ripoll, Renato Pajarola
SenVis: Interactive Tensor-based Sensitivity Visualization. Comput. Graph. Forum 40 (3) (2021)
Rafael Ballester-Ripoll, Manuele Leonelli
Global sensitivity analysis in probabilistic graphical models. CoRR (2021)
Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola
TTHRESH: Tensor Compression for Multidimensional Visual Data. IEEE Trans. Vis. Comput. Graph. 26 (9) (2020)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Sobol tensor trains for global sensitivity analysis. Reliab. Eng. Syst. Saf. 183 (2019)
Rafael Ballester-Ripoll, Renato Pajarola
Tensor Decompositions for Integral Histogram Compression and Look-Up. IEEE Trans. Vis. Comput. Graph. 25 (2) (2019)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Tensor Methods for Global Sensitivity Analysis. GI-Jahrestagung (2019)
Gaudenz Halter, Rafael Ballester-Ripoll, Barbara Flückiger, Renato Pajarola
VIAN: A Visual Annotation Tool for Film Analysis. Comput. Graph. Forum 38 (3) (2019)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Tensor Algorithms for Advanced Sensitivity Metrics. SIAM/ASA J. Uncertain. Quantification 6 (3) (2018)
Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola
TTHRESH: Tensor Compression for Multidimensional Visual Data. CoRR (2018)
Rafael Ballester-Ripoll, Renato Pajarola
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations. CoRR (2018)
Rafael Ballester-Ripoll, David Steiner, Renato Pajarola
Multiresolution Volume Filtering in the Tensor Compressed Domain. IEEE Trans. Vis. Comput. Graph. 24 (10) (2018)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Tensor Approximation of Advanced Metrics for Sensitivity Analysis. CoRR (2017)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Sobol Tensor Trains for Global Sensitivity Analysis. CoRR (2017)
Rafael Ballester-Ripoll, Renato Pajarola
Lossy volume compression using Tucker truncation and thresholding. Vis. Comput. 32 (11) (2016)
Rafael Ballester-Ripoll, Renato Pajarola
Compressing Bidirectional Texture Functions via Tensor Train Decomposition. PG (Short Papers) (2016)
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
A surrogate visualization model using the tensor train format. SIGGRAPH Asia Symposium on Visualization (2016)
Rafael Ballester-Ripoll, Susanne K. Suter, Renato Pajarola
Analysis of tensor approximation for compression-domain volume visualization. Comput. Graph. 47 (2015)
Ismael Ripoll, Rafael Ballester-Ripoll
Period Selection for Minimal Hyperperiod in Periodic Task Systems. IEEE Trans. Computers 62 (9) (2013)