Speaker
Description
Adversarial robustness is closely connected to how well classifiers capture the structure of the data distribution, motivating renewed interest in generative classifiers. We study this connection for matrix product state Born machines, tensor network models whose tractable normalization makes the learned joint density directly accessible. This tractability enables two things at once: detection and purification of adversarial inputs from the model's own density, and a continuous interpolation between discriminative and generative training that injects generative regularization into a classifier with no architectural changes. We propose two purification strategies that reuse the same trained model. Across a synthetic spirals dataset and a larger-scale MNIST benchmark, even a small generative component improves the learned density and strengthens density-based defenses over purely discriminative training. These results are a proof of principle that tensor network generative classifiers can unify robust classification, likelihood estimation, and purification within a single tractable model.
| Topic | Tensor methods and quantum-inspired machine learning |
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