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02/01/2023
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks
Sensitivity analysis measures the influence of a Bayesian network's para...
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06/22/2022
tntorch: Tensor Network Learning with PyTorch
We present tntorch, a tensor learning framework that supports multiple d...
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06/17/2022
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks
Sensitivity analysis measures the influence of a Bayesian network's para...
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05/09/2022
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Collaborative filtering models generally perform better than content-bas...
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10/07/2021
Global sensitivity analysis in probabilistic graphical models
We show how to apply Sobol's method of global sensitivity analysis to me...
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05/29/2021
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 ...
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09/11/2018
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations
Insightful visualization of multidimensional scalar fields, in particula...
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06/15/2018
TTHRESH: Tensor Compression for Multidimensional Visual Data
Memory and network bandwidth are decisive bottlenecks when handling high...
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12/05/2017
Tensor Approximation of Advanced Metrics for Sensitivity Analysis
Following up on the success of the analysis of variance (ANOVA) decompos...
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12/01/2017