3 papers
cs.LG2026
RAMP: Recognition parametrisation by Amortised Message Passing
Lior Fox, Kai Biegun, James Heald +3
A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multipl…
cs.LG2025
Maximum Likelihood Learning of Latent Dynamics Without Reconstruction
Samo Hromadka, Kai Biegun, Lior Fox +2
We introduce a novel unsupervised learning method for time series data with latent dynamical structure: the recognition-parametrized Gaussian state space model (RP-GSSM). The RP-GS…
cs.LG2024
Fast unsupervised ground metric learning with tree-Wasserstein distance
Kira M. Düsterwald, Samo Hromadka, Makoto Yamada
The performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with he…