15 citations · 19 across the 5 of their papers we have counts for
6 papers
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…
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning
Marissa Connor, Vincent Emanuele
Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such…
Learning Identity-Preserving Transformations on Data Manifolds
Marissa Connor, Kion Fallah, Christopher Rozell
Many machine learning techniques incorporate identity-preserving transformations into their models to generalize their performance to previously unseen data. These transformations…
Generative causal explanations of black-box classifiers
Matthew O'Shaughnessy, Gregory Canal, Marissa Connor +2
We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in…
Variational Autoencoder with Learned Latent Structure
Marissa C. Connor, Gregory H. Canal, Christopher J. Rozell
The manifold hypothesis states that high-dimensional data can be modeled as lying on or near a low-dimensional, nonlinear manifold. Variational Autoencoders (VAEs) approximate this…
Representing Closed Transformation Paths in Encoded Network Latent Space
Marissa Connor, Christopher Rozell
Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space. In many cases, data transformations are d…