Causal representation learning through higher-level information extraction

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Silva, F (Autor) Outra; Oliveira, HP (Autor) FCUP; Pereira, T (Autor) Outra

Revista

Título: ACM Computing Surveys

Vol. 57

Página Inicial: 43:1

ISSN: 0360-0300

Editora: ACM

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-016-ZJS

DOI: 10.1145/3696412

Abstract (EN): The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 37