---
res:
  bibo_abstract:
  - The binding problem in human cognition, concerning how the brain represents and
    connects objects within a fixed network of neural connections, remains a subject
    of intense debate. Most machine learning efforts addressing this issue in an unsupervised
    setting have focused on slot-based methods, which may be limiting due to their
    discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder
    was proposed as an alternative that learns continuous and distributed object-centric
    representations. However, it is only applicable to simple toy data. In this paper,
    we present Rotating Features, a generalization of complex-valued features to higher
    dimensions, and a new evaluation procedure for extracting objects from distributed
    representations. Additionally, we show the applicability of our approach to pre-trained
    features. Together, these advancements enable us to scale distributed object-centric
    representations from simple toy to real-world data. We believe this work advances
    a new paradigm for addressing the binding problem in machine learning and has
    the potential to inspire further innovation in the field.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sindy
      foaf_name: Löwe, Sindy
      foaf_surname: Löwe
  - foaf_Person:
      foaf_givenName: Phillip
      foaf_name: Lippe, Phillip
      foaf_surname: Lippe
  - 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: Max
      foaf_name: Welling, Max
      foaf_surname: Welling
  bibo_doi: 10.48550/arXiv.2306.00600
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_title: Rotating features for object discovery@
...