no code implementations • 15 Mar 2023 • Benjamin Girault, Eduardo Pavez, Antonio Ortega
In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data.
no code implementations • 6 Mar 2022 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator.
no code implementations • 23 Oct 2020 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs.
no code implementations • 18 Mar 2020 • Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu, Tiantian Feng, Benjamin Girault, Justin L'Hommedieu, Mackenzie Wildman, Sophia Skaaden, Amrutha Nadarajan, Jennifer L. Villatte, Tiago H. Falk, Kristina Lerman, Emilio Ferrara, Shrikanth Narayanan
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
1 code implementation • 4 Mar 2020 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient.
no code implementations • 2 Apr 2019 • Karel Mundnich, Brandon M. Booth, Benjamin Girault, Shrikanth Narayanan
In this work, we propose a novel annotation approach using triplet embeddings.