1.2k citations · 1.3k across the 7 of their papers we have counts for
7 papers
In-n-Out: Calibrating Graph Neural Networks for Link Prediction
Erik Nascimento, Diego Mesquita, Samuel Kaski +1
Deep neural networks are notoriously miscalibrated, i.e., their outputs do not reflect the true probability of the event we aim to predict. While networks for tabular or image data…
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang, Ayush Bharti, Amauri Souza +2
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics t…
Minimal Learning Machine for Multi-Label Learning
Joonas Hämäläinen, Antoine Hubermont, Amauri Souza +3
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this pa…
Distill n' Explain: explaining graph neural networks using simple surrogates
Tamara Pereira, Erik Nascimento, Lucas E. Resck +2
Explaining node predictions in graph neural networks (GNNs) often boils down to finding graph substructures that preserve predictions. Finding these structures usually implies back…
Provably expressive temporal graph networks
Amauri H. Souza, Diego Mesquita, Samuel Kaski +1
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamen…
Rethinking pooling in graph neural networks
Diego Mesquita, Amauri H. Souza, Samuel Kaski
Graph pooling is a central component of a myriad of graph neural network (GNN) architectures. As an inheritance from traditional CNNs, most approaches formulate graph pooling as a…