I try to make software as available as is practical. If you need other stuff from me, just send me an e-mail.
The key algorithms and data structures we use for working with the geometry of learned manifolds is available in StochMan.
The software for computing Grassmann Averages are available at the project webpage.
I got sick and tired of typing
plt.plot(x.detach().cpu().numpy(), y.detach().cpu().numpy(), color="green"),
so I have created a little pyplot wrapper that converts
torch tensors automatically to numpy arrays. Here's
the code; let me know if you have smarter ways of getting the same result!
The software developed for the NIPS 2012 paper used for regression and dimensionality reduction using multiple metrics can be found at the project website.
The code we use for diffeomorphic deformations is available through libcpab. Through this we provide an easy-to-use interface from numpy, pytorch and tensorflow. Both CPU and GPU computations are supported as well as automatic differentiation. So using diffeomorphisms have never been easier.
The ODE solver used both in the AISTATS 2014 and MICCAI 2014/2015 papers is being further developed at http://probabilistic-numerics.org/.
Most of the software used in our papers in articulated tracking / pose estimate / whatever-you-call-it is available at the project website.