31 citations · 189 across the 25 of their papers we have counts for
26 papers
Attraction to hierarchical feature memory explains orientation bias
Kira Michaela Düsterwald, Peter Vincent, Ana Kapros +2
When recalling the orientation of recent stimuli, observers are systematically biased away from the cardinal axes. The prevailing explanation is that this ''anti-cardinal bias'' ar…
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…
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…
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
Alexandre Galashov, Michalis K. Titsias, András György +4
Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popu…
A solution for the mean parametrization of the von Mises-Fisher distribution
Marcel Nonnenmacher, Maneesh Sahani
The von Mises-Fisher distribution as an exponential family can be expressed in terms of either its natural or its mean parameters. Unfortunately, however, the normalization functio…
A State Representation for Diminishing Rewards
Ted Moskovitz, Samo Hromadka, Ahmed Touati +2
A common setting in multitask reinforcement learning (RL) demands that an agent rapidly adapt to various stationary reward functions randomly sampled from a fixed distribution. In…