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Learning Recurrent Models with Temporally Local Rules
Fitting generative models to sequential data typically involves two recursive computations through time, one forward and one backward.
Inferring Population Dynamics in Macaque Cortex
Accordingly, Pandarinath and colleagues have introduced a benchmark to evaluate models on these two (and related) criteria: four data sets, each consisting of firing rates from a population of neurons, recorded from macaque cortex during movement-related tasks.
An Introduction to Modern Statistical Learning
There are today many internet resources that explain this or that new machine-learning algorithm in isolation, but they do not (and cannot, in so brief a space) connect these algorithms with each other or with the classical literature on statistical models, out of which the modern algorithms emerged.
Recurrent Exponential-Family Harmoniums without Backprop-Through-Time
Methods for extending RBMs--and likewise EFHs--to data with temporal dependencies have been proposed previously (Sutskever and Hinton, 2007; Sutskever et al., 2009), the learning procedure being validated by qualitative assessment of the generative model.
Sensory Integration and Density Estimation
The integration of partially redundant information from multiple sensors is a standard computational problem for agents interacting with the world.
