14 citations · 31 across the 7 of their papers we have counts for
6 papers · 1 filter
Uncovering Unique Concept Vectors through Latent Space Decomposition
Mara Graziani, Laura O' Mahony, An-Phi Nguyen +2
Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach…
It's FLAN time! Summing feature-wise latent representations for interpretability
An-phi Nguyen, Maria Rodriguez Martinez
Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decision…
Learning Invariances for Interpretability using Supervised VAE
An-phi Nguyen, María Rodríguez Martínez
We propose to learn model invariances as a means of interpreting a model. This is motivated by a reverse engineering principle. If we understand a problem, we may introduce inducti…
On quantitative aspects of model interpretability
An-phi Nguyen, María Rodríguez Martínez
Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment…
MonoNet: Towards Interpretable Models by Learning Monotonic Features
An-phi Nguyen, María Rodríguez Martínez
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data…
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez +2
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on pr…