{
  "abstract": "Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimizers for tree tensor networks (TTNs) on both their parameter and quotient manifolds, including adaptive and learning-rate-free schemes suitable for minibatch training. Using a hybrid CNN-TTN architecture, we evaluate the methods on Fashion-MNIST, CIFAR10, and Imagenette. The proposed optimizers achieve predictive performance comparable to unconstrained optimization while enabling numerically stable downstream compression.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.00870",
  "author_total": 5,
  "authors": [
    {
      "name": "Marius Willner"
    },
    {
      "name": "Maximilian Scharf"
    },
    {
      "name": "André Uschmajew"
    },
    {
      "name": "Timo Felser"
    },
    {
      "name": "Marco Trenti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wb60xe81cnyr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.00870",
  "fulltext_url": null,
  "id": "6wb60xe81cnyr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.00870",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "1702|Stochastic Gradient Optimization Techniques"
    ]
  ],
  "openalex": "W7206215057",
  "pdf_url": "https://arxiv.org/pdf/2609.00870",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6wb60xe81cnyr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:45Z",
      "native_id": "2609.00870",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.00870"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "Stochastic Optimization of Tree Tensor Networks",
  "title_text": "Stochastic Optimization of Tree Tensor Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:50Z",
  "url": "https://research.draconitic.com/paper/6wb60xe81cnyr",
  "venue": "arXiv",
  "versions": []
}