Medical image segmentation is the task of coloring in a scan, drawing a precise outline around every organ or lesion so that a clinician can measure it, track it over time, or plan around it. Done by hand it is…
G2RA-Net: Graph Slices Sharpen Medical Image Segmentation Read More »
Graph neural networks look deceptively sturdy on paper. Stack a few convolution layers, pool the node embeddings, feed the result to a classifier, and you get strong numbers on clean benchmark data. The trouble starts once the input graph stops…
HPGRL: How Hierarchical Prototypes Make Graph Classification Resist Noise Read More »
For half a century, simulating how atoms move has followed the same ritual. Work out the force on every atom, nudge each one a tiny step, then do it again, and again, millions of times over, with each step covering…
Force Free Molecular Dynamics With Equivariant Networks Read More »
Imagine a citation graph with more than seven hundred thousand papers, each carrying a bag of words feature vector and a web of references to its neighbors. Training a graph neural network on something that size can exhaust memory before…
Universal Graph Coarsening Made Fast With Hashing Read More »
Open a Julia REPL, type using GraphNeuralNetworks, and you are three lines away from training a graph convolutional network on a GPU without ever touching Python. That sentence would have sounded like wishful thinking five years ago. Carlo Lucibello and…
GraphNeuralNetworks.jl Brings Serious GNN Tooling to Julia Read More »
Multimodal intent recognition asks a system to classify what a speaker actually wants, complaining, agreeing, joking, asking for help, by combining what they said with how they said it and how they looked while saying it. Text alone captures a…
GMoE-DCAA Teaches Modalities To Cancel Out Each Other’s Noise Read More »
Clustering data that comes from more than one source, several sensors describing the same object, or several feature extractors run on the same image, is called multi-view clustering, and the whole point is to combine complementary information across those views…
How Frequency Truncation Improves Multi-View Spectral Clustering Read More »
Retail demand planning would be simple if every product existed in isolation. It does not. A grocery chain needs a forecast for total daily revenue, for each brand it stocks, for each store, and for each item on the shelf,…
Graph Neural Networks Bring Coherent Forecasts to Retail Read More »
AngioGraphCAD models coronary arteries as graphs, letting a graph neural network assess lesion-level risk the way cardiologists reason — one lesion at a time.
Jia He and Maggie X. Cheng from Illinois Institute of Technology have found a way to build GNNs with the same k-WL and k-FWL expressive power as the best known high-order networks — using a fraction of the parameters and…
Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power Read More »