1 citations · 1 across the 1 of their papers we have counts for
6 papers
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…
Active Ordinal Querying for Tuplewise Similarity Learning
Gregory Canal, Stefano Fenu, Christopher Rozell
Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similar…
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems
John Lee, Nicholas P. Bertrand, Christopher J. Rozell
The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computational imaging is the optimal transport framework.…
Hierarchical Optimal Transport for Multimodal Distribution Alignment
John Lee, Max Dabagia, Eva L. Dyer +1
In many machine learning applications, it is necessary to meaningfully aggregate, through alignment, different but related datasets. Optimal transport (OT)-based approaches pose al…
Active embedding search via noisy paired comparisons
Gregory H. Canal, Andrew K. Massimino, Mark A. Davenport +1
Suppose that we wish to estimate a user's preference vector from paired comparisons of the form "does user prefer item or item ?," where both the user and items are…
Sparse Bayesian Learning with Dynamic Filtering for Inference of Time-Varying Sparse Signals
Matthew R. O'Shaughnessy, Mark A. Davenport, Christopher J. Rozell
Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an a…