2 citations · 2 across the 4 of their papers we have counts for
5 papers
Learning Hamiltonians from Trajectory Data with Kernel Methods
Jianyu Hu, David Martin de Diego, Juan-Pablo Ortega +1
We study the recovery of a Hamiltonian from discrete trajectory observations using structure-preserving kernel methods. When exact-flow data are fitted through a symplectic one-ste…
A kernel method for the learning of Wasserstein geometric flows
Jianyu Hu, Juan-Pablo Ortega, Daiying Yin
Wasserstein gradient and Hamiltonian flows have emerged as essential tools for modeling complex dynamics in the natural sciences, providing a unifying geometric formulation of many…
A global structure-preserving kernel method for the learning of Poisson systems
Jianyu Hu, Juan-Pablo Ortega, Daiying Yin
A structure-preserving kernel ridge regression method is presented that allows the recovery of globally defined, potentially high-dimensional, and nonlinear Hamiltonian functions o…
A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
Jianyu Hu, Juan-Pablo Ortega, Daiying Yin
A structure-preserving kernel ridge regression method is presented that allows the recovery of nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamilto…
Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives
Marcel Hirt, Domenico Campolo, Victoria Leong +1
Devising deep latent variable models for multi-modal data has been a long-standing theme in machine learning research. Multi-modal Variational Autoencoders (VAEs) have been a popul…