Search Results for author:
Found 6 papers, 1 papers with code
It's FLAN time! Summing feature-wise latent representations for interpretability
Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e. g. legal system, healthcare.
On quantitative aspects of model interpretability
Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies.
Learning Invariances for Interpretability using Supervised VAE
If we understand a problem, we may introduce inductive biases in our model in the form of invariances.
MonoNet: Towards Interpretable Models by Learning Monotonic Features
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance.
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
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.
Ranked #28 on
Graph Classification
on MUTAG
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective.
