1 code implementation • 12 Sep 2023 • Enrique Alvarado, Robin Belton, Emily Fischer, Kang-Ju Lee, Sourabh Palande, Sarah Percival, Emilie Purvine
The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset.
no code implementations • 1 Dec 2022 • Emilie Purvine, Davis Brown, Brett Jefferson, Cliff Joslyn, Brenda Praggastis, Archit Rathore, Madelyn Shapiro, Bei Wang, Youjia Zhou
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures.
no code implementations • 14 Aug 2022 • Brenda Praggastis, Davis Brown, Carlos Ortiz Marrero, Emilie Purvine, Madelyn Shapiro, Bei Wang
Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map.
no code implementations • 3 Apr 2022 • R. W. R. Darling, John A. Emanuello, Emilie Purvine, Ahmad Ridley
Topological Data Analysis (TDA) is a rigorous framework that borrows techniques from geometric and algebraic topology, category theory, and combinatorics in order to study the "shape" of such complex high-dimensional data.
no code implementations • 21 May 2021 • Henry Kvinge, Brett Jefferson, Cliff Joslyn, Emilie Purvine
As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical.
no code implementations • 17 Nov 2020 • Sarah Tymochko, Zachary New, Lucius Bynum, Emilie Purvine, Timothy Doster, Julien Chaput, Tegan Emerson
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks.
no code implementations • 6 Oct 2020 • Song Feng, Emily Heath, Brett Jefferson, Cliff Joslyn, Henry Kvinge, Hugh D. Mitchell, Brenda Praggastis, Amie J. Eisfeld, Amy C. Sims, Larissa B. Thackray, Shufang Fan, Kevin B. Walters, Peter J. Halfmann, Danielle Westhoff-Smith, Qing Tan, Vineet D. Menachery, Timothy P. Sheahan, Adam S. Cockrell, Jacob F. Kocher, Kelly G. Stratton, Natalie C. Heller, Lisa M. Bramer, Michael S. Diamond, Ralph S. Baric, Katrina M. Waters, Yoshihiro Kawaoka, Jason E. McDermott, Emilie Purvine
Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions.
no code implementations • 12 Jun 2019 • Sinan G. Aksoy, Kathleen E. Nowak, Emilie Purvine, Stephen J. Young
Similarity measures are used extensively in machine learning and data science algorithms.