Il Memming Park | DeepAI

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Featured Co-authors

Mijung Park
25 publications
Lars Buesing
20 publications
Yuan Zhao
19 publications
Eva L. Dyer
19 publications
Liam Paninski
17 publications
SueYeon Chung
16 publications
Marcel Nassar
13 publications
Anqi Wu
10 publications
Jonathan W. Pillow
9 publications
Chethan Pandarinath
7 publications
Josue Nassar
5 publications

∙ 08/24/2023

Persistent learning signals and working memory without continuous attractors

Neural dynamical systems with stable attractor structures, such as point...

0 Il Memming Park, et al. ∙

∙ 06/01/2023

Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains

Latent Gaussian process (GP) models are widely used in neuroscience to u...

0 Matthew Dowling, et al. ∙

∙ 05/18/2023

Real-Time Variational Method for Learning Neural Trajectory and its Dynamics

Latent variable models have become instrumental in computational neurosc...

0 Matthew Dowling, et al. ∙

∙ 03/03/2023

Spectral learning of Bernoulli linear dynamical systems models for decision-making

Latent linear dynamical systems with Bernoulli observations provide a po...

0 Iris R. Stone, et al. ∙

∙ 09/09/2021

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity

Advances in neural recording present increasing opportunities to study n...

11 Felix Pei, et al. ∙

∙ 07/15/2021

Hida-Matérn Kernel

We present the class of Hida-Matérn kernels, which is the canonical fami...

0 Matthew Dowling, et al. ∙

∙ 12/08/2020

On 1/n neural representation and robustness

Understanding the nature of representation in neural networks is a goal ...

5 Josue Nassar, et al. ∙

∙ 10/23/2020

Rescuing neural spike train models from bad MLE

The standard approach to fitting an autoregressive spike train model is ...

0 Diego M. Arribas, et al. ∙

∙ 09/02/2020

Non-parametric generalized linear model

A fundamental problem in statistical neuroscience is to model how neuron...

9 Matthew Dowling, et al. ∙

∙ 06/04/2019

Streaming Variational Monte Carlo

Nonlinear state-space models are powerful tools to describe dynamical st...

0 Yuan Zhao, et al. ∙

∙ 06/03/2019

Gated recurrent units viewed through the lens of continuous time dynamical systems

Gated recurrent units (GRUs) are specialized memory elements for buildin...

1 Ian D. Jordan, et al. ∙

∙ 11/29/2018

Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling

Many real-world systems studied are governed by complex, nonlinear dynam...

8 Josue Nassar, et al. ∙

∙ 10/09/2018

Information Geometry of Orthogonal Initializations and Training

Recently mean field theory has been successfully used to analyze propert...

0 Piotr A. Sokol, et al. ∙

∙ 07/27/2017

Recursive Variational Bayesian Dual Estimation for Nonlinear Dynamics and Non-Gaussian Observations

State space models provide an interpretable framework for complex time s...

0 Yuan Zhao, et al. ∙

∙ 04/11/2016

Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

When governed by underlying low-dimensional dynamics, the interdependenc...

0 Yuan Zhao, et al. ∙

∙ 11/23/2015

Black box variational inference for state space models

Latent variable time-series models are among the most heavily used tools...

0 Evan Archer, et al. ∙

∙ 02/09/2012

Active Bayesian Optimization: Minimizing Minimizer Entropy

The ultimate goal of optimization is to find the minimizer of a target f...

0 Il Memming Park, et al. ∙

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