Research explainers on graph neural networks — GCNs, GATs, message passing, and graph-based representation learning.
Vision transformers earned their place in medical imaging because self attention lets a model connect a finding in one part of an image to context somewhere else entirely, the kind of long range reasoning a small convolution kernel cannot do…
FadeFormer: Graph Diffusion Sharpens Medical Image Classification Read More »
Wind speed forecasting has a long history built on physical simulation, statistical time series methods, and more recently deep networks. Global wind capacity keeps climbing, and China alone added 80 gigawatts in 2024, which the paper notes was roughly seventy…
Adaptive Graph Attention Improves Wind Vector Forecasting Read More »
Unsupervised image segmentation asks a model to draw meaningful boundaries around objects in a photo without ever being told what those objects are. No labeled training data, no annotated masks, just pixels in and a partition of the image out.…
DeepCut++’s Single Best Fusion Weight Does Not Actually Win Every Task Read More »
Ask a radiology resident to find the spleen on a single unfamiliar scan after seeing only one labeled example beforehand and you are asking for exactly the problem this paper takes on, except the resident here is a neural network.…
Prototype Guided Graph Reasoning for Few Shot Medical Segmentation Read More »
Picture a strip of land seen through radar instead of a camera. A river cuts a clean line through farmland, a cluster of buildings sits in a blocky grid, and a forest spreads out as a grainy, speckled mess. To…
Manifold Aware Fusion for PolSAR Image Classification Read More »
Transformers rewrote what was possible in language and vision, so pointing them at graphs looked like a sure bet. The bet mostly paid off on small graphs and mostly stalled on large ones. The reason is simple to state and…
A Graph Transformer That Scales to Billions of Nodes 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 »
Brain network analysis usually works like this. Take a resting state fMRI scan, split the brain into a fixed number of regions using an anatomical atlas such as AAL with its 116 regions, measure how correlated each region’s activity is…
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 »