98 citations · 224 across the 9 of their papers we have counts for
9 papers
Non-parametric generalized linear model
Matthew Dowling, Yuan Zhao, Il Memming Park
A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to…
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Josue Nassar, Scott W. Linderman, Monica Bugallo +1
Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how…
Information Geometry of Orthogonal Initializations and Training
Piotr A. Sokol, Il Memming Park
Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It gave rise to a prescriptive theory for initializing feed-forward neu…
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Yuan Zhao, Il Memming Park
A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing…
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
Yuan Zhao, Il Memming Park
When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded population of neurons can be explained by a small number of shared factors, or…
Bayesian Extensions of Kernel Least Mean Squares
Il Memming Park, Sohan Seth, Steven Van Vaerenbergh
The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algor…
Kernel methods on spike train space for neuroscience: a tutorial
Il Memming Park, Sohan Seth, Antonio R. C. Paiva +2
Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain…
Bayesian Entropy Estimation for Countable Discrete Distributions
Evan Archer, Il Memming Park, Jonathan Pillow
We consider the problem of estimating Shannon's entropy from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor…
Active Bayesian Optimization: Minimizing Minimizer Entropy
Il Memming Park, Marcel Nassar, Mijung Park
The ultimate goal of optimization is to find the minimizer of a target function.However, typical criteria for active optimization often ignore the uncertainty about the minimizer.…