most citedEvaluating the state-of-the-art in mapping research spaces: a Brazilian case study

4 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Context Window Failures in Relational Foundation Models

Denis Oliveira Correa, Francisco Galuppo Azevedo

Recent Relational Deep Learning architectures have been proposed as foundation models for multi-table relational data, yet they impose constrained neighborhood budgets that force r…

cs.LG2026

Can LLMs Use Relational Transformer Embeddings?

Francisco Galuppo Azevedo, Clarissa Lima Loures

Injecting frozen relational-encoder embeddings as soft tokens into a large language model (LLM) is a conceptually appealing fusion strategy: the encoder handles multi-table structu…

cs.LG2026

Real-Time Explanations for Tabular Foundation Models

Luan Borges Teodoro Reis Sena, Francisco Galuppo Azevedo

Interpretability is central for scientific machine learning, as understanding \emph{why} models make predictions enables hypothesis generation and validation. While tabular foundat…

cs.LG2026

Task Scarcity and Label Leakage in Relational Transfer Learning

Francisco Galuppo Azevedo, Clarissa Lima Loures, Denis Oliveira Correa

Training relational foundation models requires learning representations that transfer across tasks, yet available supervision is typically limited to a small number of prediction t…

cs.DL2021★ 4 cited

Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study

Francisco Galuppo Azevedo, Fabricio Murai

Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network. Understanding how research areas are connected is of paramount importance for ad…