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Found 5 papers, 1 papers with code
Deep and shallow data science for multi-scale optical neuroscience
Optical imaging of the brain has expanded dramatically in the past two decades.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Matrix-normal models for fMRI analysis
We show how the matrix-variate normal (MN) formalism can unify some of these methods into a single framework.
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs.
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.
