7 citations · 13 across the 6 of their papers we have counts for
6 papers
Model editing for distribution shifts in uranium oxide morphological analysis
Davis Brown, Cody Nizinski, Madelyn Shapiro +4
Deep learning still struggles with certain kinds of scientific data. Notably, pretraining data may not provide coverage of relevant distribution shifts (e.g., shifts induced via th…
Attributing Learned Concepts in Neural Networks to Training Data
Nicholas Konz, Charles Godfrey, Madelyn Shapiro +3
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data. As having the right (or…
Topological Analysis of Temporal Hypergraphs
Audun Myers, Cliff Joslyn, Bill Kay +3
In this work we study the topological properties of temporal hypergraphs. Hypergraphs provide a higher dimensional generalization of a graph that is capable of capturing multi-way…
Experimental Observations of the Topology of Convolutional Neural Network Activations
Emilie Purvine, Davis Brown, Brett Jefferson +6
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of compl…
The SVD of Convolutional Weights: A CNN Interpretability Framework
Brenda Praggastis, Davis Brown, Carlos Ortiz Marrero +3
Deep neural networks used for image classification often use convolutional filters to extract distinguishing features before passing them to a linear classifier. Most interpretabil…
Optimal renormalization of multi-scale systems
Jacob Price, Brek Meuris, Madelyn Shapiro +1
While model order reduction is a promising approach in dealing with multi-scale time-dependent systems that are too large or too expensive to simulate for long times, the resulting…