Visualization for Machine Learning

Autor(es)

Xenopoulos, Peter ; Nonato, Luis Gustavo ; Silva, Claudio ; DeCarvalho, BM ; Goncalves, LMG

• Tipo de documento: Artigo Científico
• Fonte: 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022); v. N/A, p. 8-pg., 2022-01-01.

Resumo

As machine learning has increased in popularity, visualization has taken an important role in analyzing and communicating aspects of machine learning models. Increasingly, visualization techniques are being used across a broad set of domains and in business-critical use cases. Oftentimes, these visualizations convey non-trivial machine learning concepts, utilize complex visual representations, or demand user interaction. In this tutorial, we seek to provide a foundational understanding, to a broad audience, of the ways in which we can use visualization for machine learning tasks. In particular, we detail visual techniques for model assessment, model understanding, and dimensionality reduction. Furthermore, we outline foundations and recent work in emerging visualization topics such as topological data analysis and understanding deep learning model internals. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs