110 citations · 298 across the 13 of their papers we have counts for
6 papers · 1 filter
Real-Time Variational Method for Learning Neural Trajectory and its Dynamics
Matthew Dowling, Yuan Zhao, Il Memming Park
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorith…
Spectral learning of Bernoulli linear dynamical systems models
Iris R. Stone, Yotam Sagiv, Il Memming Park +1
Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which ar…
Rescuing neural spike train models from bad MLE
Diego M. Arribas, Yuan Zhao, Il Memming Park
The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction. This maximum likelihood estimation (MLE) often leads to…
Streaming Variational Monte Carlo
Yuan Zhao, Josue Nassar, Ian Jordan +2
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simul…
Variational online learning of neural dynamics
Yuan Zhao, Il Memming Park
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underl…
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure…