This is a PyTorch implementation of the spline-based convolution operator of SplineCNN, as described in our paper: Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller: SplineCNN: Fast ...

The intersection between “woodworkers” and “programmers” is not a densely populated part of the Venn diagram, but [Michael Schiebler] is there with his Kerf Bend Wizard to help us make wood twist and ...

Computational research tools have reached a level of maturity that enables efficient simulation of neural activity across diverse scales. Concurrently, experimental neuroscience is experiencing an ...

This repository gathers all known Kolmogorov-Arnold Networks (including those I developed) from various sources. These networks are implemented for image classification on some simple image ...

Working memory is central to cognition. It allows us to remember multiple items at once, and use those items to guide future behavior—such as remembering the items on a grocery list. However, working ...

Thin Plate Splines (TPS) are a type of radial basis function (RBF) commonly used in image warping, shape interpolation, and spatial deformation tasks. They were introduced by Bookstein in 1989 as a ...

In the previous article, I presented a data extraction tool and the strategy I generally use on the data extraction for interpolation. Besides that, I discussed some linear interpolation methods ...

Predicting the stability of granular materials under particle removal has wide-reaching applications, including automating tunnel excavations. Searching for general laws that govern granular stability ...

Neuromorphology is crucial to identifying neuronal subtypes and understanding learning. It is also implicated in neurological disease. However, standard morphological analysis focuses on macroscopic ...