Causal representation learning through higher-level information extraction

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Silva, F (Author) Other; Oliveira, HP (Author) FCUP; Pereira, T (Author) Other

Journal

Title: ACM Computing Surveys

Vol. 57

Initial page: 43:1

ISSN: 0360-0300

Publisher: ACM

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 37