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.
Swarms on Manifolds for Deep Learning: Training Kuramoto Models and Trajectory Learning
4 Feb 2026
Discover parameter estimation for wrapped Cauchy and von Mises distributions in trajectory learning.
Probabilistic ML on Grassmannians and Orthogonal Groups: Langevin and Bingham Matrix Models
4 Feb 2026
Master statistical ML on Grassmannians and orthogonal groups. Learn how matrix Bingham and matrix Langevin distributions derive from vMF spherical models.
Hyperbolic Space Statistical Models: Geometric Deep Learning & Inference
4 Feb 2026
Discover how statistical models over hyperbolic spaces enable inference, sampling, and density estimation in Geometric Deep Learning
Probabilistic Learning on Spheres: von Mises-Fisher, Spherical Cauchy, and Bingham Distributions
3 Feb 2026
Explore statistical models for spheres in Machine Learning. Learn about vMF, Bingham, and Poisson kernel distributions for unsupervised learning and RL.