4 citations · 6 across the 7 of their papers we have counts for
7 papers · 1 filter
Measuring Structure in Graph Benchmark Datasets Using Graph Invariants
Richard von Moos, Mathieu Alain, Bastian Rieck
Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard to tell whether a model actually…
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
Mathieu Alain, So Takao, Xiaowen Dong +2
Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs su…
Graph Classification Gaussian Processes via Hodgelet Spectral Features
Mathieu Alain, So Takao, Xiaowen Dong +2
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed…
MANTRA: The Manifold Triangulations Assemblage
Rubén Ballester, Ernst Röell, Daniel Bīn Schmid +4
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting higher-order structures in the data,…
Gaussian Processes on Cellular Complexes
Mathieu Alain, So Takao, Brooks Paige +1
In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological inductive biases. In particular, recent attention h…
Graph Neural Networks Go Forward-Forward
Daniele Paliotta, Mathieu Alain, Bálint Máté +1
We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows…
Out-of-distribution detection for regression tasks: parameter versus predictor entropy
Yann Pequignot, Mathieu Alain, Patrick Dallaire +4
It is crucial to detect when an instance lies downright too far from the training samples for the machine learning model to be trusted, a challenge known as out-of-distribution (OO…