14 citations · 20 across the 3 of their papers we have counts for
5 papers
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…
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
Ivan Girardi, Pengfei Ji, An-phi Nguyen +5
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issu…