How to Represent Part-Whole Hierarchies in a Neural Network.
Neural Comput
; 35(3): 413-452, 2023 Feb 17.
Article
en En
| MEDLINE
| ID: mdl-36543334
ABSTRACT
This article does not describe a working system. Instead, it presents a single idea about representation that allows advances made by several different groups to be combined into an imaginary system called GLOM.1 The advances include transformers, neural fields, contrastive representation learning, distillation, and capsules. GLOM answers the question How can a neural network with a fixed architecture parse an image into a part-whole hierarchy that has a different structure for each image? The idea is simply to use islands of identical vectors to represent the nodes in the parse tree. If GLOM can be made to work, it should significantly improve the interpretability of the representations produced by transformer-like systems when applied to vision or language.
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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Neural Comput
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article