Robotics Motion Learning: Training Linked Robot Arms with Kuramoto Models
11 Feb 2026
Explore deterministic and stochastic policies, normalizing flows on tori, and Kuramoto networks for motion prediction.
Wahba’s Problem and SO(3) Optimization: Rotation Learning in Geometric ML
11 Feb 2026
Learn about stochastic policies on manifolds using Bingham, Cauchy, and von Mises-Fisher parametrizations.
Beyond Kuramoto Models: Associative Memory and Plastic Synapses in ML Ensembles
11 Feb 2026
Learn about associative memories, Ising model generalizations, and Hebbian learning in networks with plastic synapses.
Grassmannian Manifold Learning: Optimization and Deep Learning Architectures
11 Feb 2026
Learn about Riccati ODEs and matrix-based deep neural networks.
Learning Coupled Actions of Lie Groups: Kuramoto Models for Robotics and Hyperbolic Data
10 Feb 2026
Explore applications in robotics, computational physics, and hyperbolic geometry optimization.
Statistical Models for the Latent Space: From Gaussian VAE to Kuramoto-Enhanced S-VAE
10 Feb 2026
Learn about Gaussian VAE, Categorical VAE (CAT-VAE), and emerging Spherical VAEs using Kuramoto networks.
Unsupervised Learning on Manifolds: Spherical Clustering and Kuramoto Ensembles
10 Feb 2026
Learn about spherical k-means clustering, mixtures of von Mises-Fisher distributions, and dual data encoding in Kuramoto models.
Reinforcement Learning on Non-Euclidean Spaces: Swarms, Spheres, and Hyperbolic RL
4 Feb 2026
Learn about stochastic policies using Bingham, spherical Cauchy, and hyperbolic latent representations.
Supervised Learning for Swarms on Manifolds: Training Kuramoto Networks and Stochastic Optimization
4 Feb 2026
Explore Maximum Likelihood, Score Matching, and Evolutionary Optimization (CMA ES) on manifolds.