Smart Data Grouping: Organizing Networks Without Guesswork
13 Feb 2026
Learn how to perform deep graph clustering without a predefined cluster number K using Lorentz hyperbolic models and H-dimensional structural information.
LSEnet: A Smarter Way to Organize Data Using Curved Space
13 Feb 2026
Learn how H-dimensional Structural Entropy minimizes uncertainty and enables optimization without a predefined cluster number.
Generalized Kuramoto Models & Hyperbolic Graph Clustering in Lorentz Space
13 Feb 2026
Learn about generalized Kuramoto oscillators on unit spheres and hierarchical clustering within the Lorentz model of hyperbolic space.
Riemannian Graph Learning & Structural Entropy: Deep Node Clustering in 2026
13 Feb 2026
Learn how Hyperbolic Riemannian manifolds and Lorentz models solve complex node clustering with unknown cluster numbers.
LSEnet: Deep Graph Clustering with Unknown Cluster Numbers via Lorentz Hyperbolic Space
13 Feb 2026
Discover LSEnet, a novel deep graph clustering model that solves the problem of unknown cluster numbers using Differentiable Structural Information (DSI)
Spatial Rotation Learning for Robotic Arms: SO(3) vs. Bingham Distributions
12 Feb 2026
Learn how to solve the gimbal loop problem in 3D motion modeling.
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.