7 citations · 10 across the 4 of their papers we have counts for
4 papers
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose d…
Tensorization of neural networks for improved privacy and interpretability
José Ramón Pareja Monturiol, Alejandro Pozas-Kerstjens, David Pérez-García
We present a tensorization algorithm for constructing tensor train/matrix product state (MPS) representations of functions, drawing on sketching and cross interpolation ideas. The…
TensorKrowch: Smooth integration of tensor networks in machine learning
José Ramón Pareja Monturiol, David Pérez-García, Alejandro Pozas-Kerstjens
Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed…
Privacy-preserving machine learning with tensor networks
Alejandro Pozas-Kerstjens, Senaida Hernández-Santana, José Ramón Pareja Monturiol +4
Tensor networks, widely used for providing efficient representations of low-energy states of local quantum many-body systems, have been recently proposed as machine learning archit…