{
  "abstract": "Initialization plays an important role in neural network training. A classical way to address this task relies on Gaussian weights and preactivations, motivated in part by their analytical tractability and their emergence in large-width approximations. We propose an activation-induced initialization based on a statistical-mechanical viewpoint: for activations that arise as the gradient of a Legendre potential, the latter defines a Gibbs family of preactivation distributions. This perspective allows the preactivation distribution, rather than only its scale, to be adapted to the activation geometry.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Diego Pinto-Suárez"
    },
    {
      "name": "Jordan Patracone"
    },
    {
      "name": "Marc Sebban"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4cxnc79jrv936.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "4cxnc79jrv936",
  "issns": [],
  "landing_url": "https://hal.science/hal-05789261v2",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.math"
    ],
    [
      "hal",
      "0.stat"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05789261/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4cxnc79jrv936.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:59Z",
      "native_id": "hal-05789261",
      "source": "hal",
      "url": "https://hal.science/hal-05789261v2"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    }
  ],
  "title": "Activation-Matched Gibbs Initialization for Neural Networks",
  "title_text": "Activation-Matched Gibbs Initialization for Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:59Z",
  "url": "https://research.draconitic.com/paper/4cxnc79jrv936",
  "venue": "HAL",
  "versions": []
}