3 papers
cs.LG2025
CID: Measuring Feature Importance Through Counterfactual Distributions
Eddie Conti, Álvaro Parafita, Axel Brando
Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous methods exist, the lack of a defin…
cs.LG2025
Probing the Embedding Space of Transformers via Minimal Token Perturbations
Eddie Conti, Alejandro Astruc, Alvaro Parafita +1
Understanding how information propagates through Transformer models is a key challenge for interpretability. In this work, we study the effects of minimal token perturbations on th…
cs.CL2024
An alternative formulation of attention pooling function in translation
Eddie Conti
The aim of this paper is to present an alternative formulation of the attention scoring function in translation tasks. Generally speaking, language is deeply structured, and this i…