---
res:
  bibo_abstract:
  - "Learning modular object-centric representations is crucial for systematic generalization.
    Existing methods show promising object-binding capabilities empirically,\r\nbut
    theoretical identifiability guarantees remain relatively underdeveloped. Understanding
    when object-centric representations can theoretically be identified is\r\ncrucial
    for scaling slot-based methods to high-dimensional images with correctness\r\nguarantees.
    To that end, we propose a probabilistic slot-attention algorithm that\r\nimposes
    an aggregate mixture prior over object-centric slot representations, thereby\r\nproviding
    slot identifiability guarantees without supervision, up to an equivalence\r\nrelation.
    We provide empirical verification of our theoretical identifiability result\r\nusing
    both simple 2-dimensional data and high-resolution imaging datasets.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Avinash
      foaf_name: Kori, Avinash
      foaf_surname: Kori
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Locatello, Francesco
      foaf_surname: Locatello
      foaf_workInfoHomepage: http://www.librecat.org/personId=26cfd52f-2483-11ee-8040-88983bcc06d4
    orcid: 0000-0002-4850-0683
  - foaf_Person:
      foaf_givenName: Ainkaran
      foaf_name: Santhirasekaram, Ainkaran
      foaf_surname: Santhirasekaram
  - foaf_Person:
      foaf_givenName: Francesca
      foaf_name: Toni, Francesca
      foaf_surname: Toni
  - foaf_Person:
      foaf_givenName: Ben
      foaf_name: Glocker, Ben
      foaf_surname: Glocker
  - foaf_Person:
      foaf_givenName: Fabio
      foaf_name: De Sousa Ribeiro, Fabio
      foaf_surname: De Sousa Ribeiro
  bibo_volume: 37
  dct_date: 2024^xs_gYear
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Identifiable object-centric representation learning via probabilistic
    slot attention@
...