84 citations · 342 across the 31 of their papers we have counts for
34 papers
Distance preservation in state-space methods for detecting causal interactions in dynamical systems
Matthew O'Shaughnessy, Mark Davenport, Christopher Rozell
We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems. These algorithms are often justified by Takens' embedding…
Manifold Contrastive Learning with Variational Lie Group Operators
Kion Fallah, Alec Helbling, Kyle A. Johnsen +1
Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed mo…
PrefGen: Preference Guided Image Generation with Relative Attributes
Alec Helbling, Christopher J. Rozell, Matthew O'Shaughnessy +1
Deep generative models have the capacity to render high fidelity images of content like human faces. Recently, there has been substantial progress in conditionally generating image…
Learning Internal Representations of 3D Transformations from 2D Projected Inputs
Marissa Connor, Bruno Olshausen, Christopher Rozell
When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images. It has been shown that humans…
Active Learning of Ordinal Embeddings: A User Study on Football Data
Christoffer Loeffler, Kion Fallah, Stefano Fenu +4
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in informat…
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…