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.LG2026
RotRNN: Modelling Long Sequences with Rotations
Kai Biegun, Rares Dolga, Jake Cunningham +1
Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling b…
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…