176 citations · 201 across the 11 of their papers we have counts for
3 papers · 1 filter
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…