TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.8.4.095
TI  - Tensorization of neural networks for improved privacy and interpretability
PY  - 2025/12/24
UR  - https://www.scipost.org/SciPostPhysCore.8.4.095
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 8
IS  - 4
SP  - 095
A1  - Pareja Monturiol, José Ramón
AU  - Pozas-Kerstjens, Alejandro
AU  - Pérez-García, David
AB  - We present a tensorization algorithm for constructing tensor train/matrix product state (MPS) representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the MPS representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing MPS in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks.
ER  -

@Article{10.21468/SciPostPhysCore.8.4.095,
	title={{Tensorization of neural networks for improved privacy and interpretability}},
	author={José Ramón Pareja Monturiol and Alejandro Pozas-Kerstjens and David Pérez-García},
	journal={SciPost Phys. Core},
	volume={8},
	pages={095},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.8.4.095},
	url={https://scipost.org/10.21468/SciPostPhysCore.8.4.095},
}