31 citations · 189 across the 25 of their papers we have counts for
7 papers · 1 filter
Prediction under Latent Subgroup Shifts with High-Dimensional Observations
William I. Walker, Arthur Gretton, Maneesh Sahani
We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved c…
Amortised Learning by Wake-Sleep
Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa +1
Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning f…
A neurally plausible model learns successor representations in partially observable environments
Eszter Vertes, Maneesh Sahani
Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent…
Learning interpretable continuous-time models of latent stochastic dynamical systems
Lea Duncker, Gergo Bohner, Julien Boussard +1
We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at u…
Flexible and accurate inference and learning for deep generative models
Eszter Vertes, Maneesh Sahani
We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibili…
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar +3
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their…