Search Results for author:
Found 7 papers, 3 papers with code
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
Recurrent neural networks (RNNs) are a widely used tool for sequential data analysis, however, they are still often seen as black boxes of computation.
CrEIMBO: Cross Ensemble Interactions in Multi-view Brain Observations
We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations).
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging.
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
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 intra-state variability.
Multi-Lingual DALL-E Storytime
While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations.
Human or Machine? Turing Tests for Vision and Language
To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
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 behavior.
