IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/2x3wh_v1.html
   My bibliography  Save this paper

The Generative Forests

Author

Listed:
  • Chen, Sherlock Allan

Abstract

Masked autoencoder not only can be used to reconstruct images, but also can deal with videos. The only question is that how can we design such a model to achieve this purpose. It is this question, I will address in this paper. Specifically, I propose a theoretical notion - "\textbf{The Generative Spacetime}", which from the first principle, unifies the concepts of generative models in deep learning and that of Minkowski spacetime in Einstein's theory of special relativity. From the top-down physical perspective, I try to intuitively model the classical autoencoder architecture\cite{ae} as a light cone diagram to describe what we have known, by analyzing its geometric structure. Moreover, the idea of \textbf{the Generative Forests} is also derived from the geometric properties of the generative spacetime. And this work will pave the way for my future work, which is about the \textit{Hilbate Machine}.

Suggested Citation

  • Chen, Sherlock Allan, 2025. "The Generative Forests," OSF Preprints 2x3wh_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:2x3wh_v1
    DOI: 10.31219/osf.io/2x3wh_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6821a92ade2f205d3d7a1be0/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/2x3wh_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:2x3wh_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.