176 citations · 201 across the 11 of their papers we have counts for
12 papers
BERS: Locally Optimal Continuous Algorithm for Maritime Weather Routing with Just-in-Time Arrival
Daniel Precioso, Francisco Suárez, Javier Jiménez de la Jara +2
Maritime weather routing must optimize route geometry under dynamic wind-wave conditions, obstacle constraints, and fixed-arrival requirements. We present Bézier Evolve and Refine…
Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks
Rafael Ballester-Ripoll, Manuele Leonelli
Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT…
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks
Rafael Ballester-Ripoll, Manuele Leonelli
Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a spec…
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks
Rafael Ballester-Ripoll, Manuele Leonelli
Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a spec…
tntorch: Tensor Network Learning with PyTorch
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our li…
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll +2
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario coll…
Global sensitivity analysis in probabilistic graphical models
Rafael Ballester-Ripoll, Manuele Leonelli
We show how to apply Sobol's method of global sensitivity analysis to measure the influence exerted by a set of nodes' evidence on a quantity of interest expressed by a Bayesian ne…
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll +3
We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network enc…
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations
Rafael Ballester-Ripoll, Renato Pajarola
Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many fields in computational science and engineering. We propose a principal c…
TTHRESH: Tensor Compression for Multidimensional Visual Data
Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola
Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable…
Tensor Approximation of Advanced Metrics for Sensitivity Analysis
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Following up on the success of the analysis of variance (ANOVA) decomposition and the Sobol indices (SI) for global sensitivity analysis, various related quantities of interest hav…
Sobol Tensor Trains for Global Sensitivity Analysis
Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
Sobol indices are a widespread quantitative measure for variance-based global sensitivity analysis, but computing and utilizing them remains challenging for high-dimensional system…