6 citations · 16 across the 6 of their papers we have counts for
6 papers
Neural FIM for learning Fisher Information Metrics from point cloud data
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong +5
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffus…
Graph Fourier MMD for Signals on Graphs
Samuel Leone, Aarthi Venkat, Guillaume Huguet +3
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such…
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Guillaume Huguet, Alexander Tong, Edward De Brouwer +4
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Su…
Learnable Filters for Geometric Scattering Modules
Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar +5
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.…
ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins
Egbert Castro, Abhinav Godavarthi, Julian Rubinfien +3
The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mu…
Learning shared neural manifolds from multi-subject FMRI data
Jessie Huang, Erica L. Busch, Tom Wallenstein +6
Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environment…