13 citations · 21 across the 11 of their papers we have counts for
4 papers · 1 filter
Neighbor Embedding for High-Dimensional Sparse Poisson Data
Noga Mudrik, Adam S. Charles
Across many scientific fields, measurements often represent the number of times an event occurs. For example, a document can be represented by word occurrence counts, neural activi…
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Yenho Chen, Noga Mudrik, Kyle A. Johnsen +3
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models…
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam S. Charles
Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and in…
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik, Yenho Chen, Eva Yezerets +2
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and beha…