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Found 10 papers, 8 papers with code
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks
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 specific value.
tntorch: Tensor Network Learning with PyTorch
We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface.
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks
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 specific value.
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave.
Global sensitivity analysis in probabilistic graphical models
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 network.
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only.
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations
Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many fields in computational science and engineering.
TTHRESH: Tensor Compression for Multidimensional Visual Data
Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies.
Graphics
Tensor Approximation of Advanced Metrics for Sensitivity Analysis
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 have been defined in the literature including the effective and mean dimensions, the dimension distribution, and the Shapley values.
Numerical Analysis 65C20, 15A69, 49Q12
Sobol Tensor Trains for Global Sensitivity Analysis
We propose the tensor train decomposition (TT) as a unified framework for surrogate modeling and global sensitivity analysis via Sobol indices.
Numerical Analysis 65C20, 15A69, 49Q12
