activity
20182022
most citedProceedings of TDA: Applications of Topological Data Analysis to Data Science, Artificial Intelligence, and Machine Learning Workshop at SDM 2022

1 citations · 2 across the 7 of their papers we have counts for

collaborators

14 papers

cs.LG2022

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…

math.AT20221 cited

Proceedings of TDA: Applications of Topological Data Analysis to Data Science, Artificial Intelligence, and Machine Learning Workshop at SDM 2022

R. W. R. Darling, John A. Emanuello, Emilie Purvine +1

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 "sha…

cs.LG2021

Sheaves as a Framework for Understanding and Interpreting Model Fit

Henry Kvinge, Brett Jefferson, Cliff Joslyn +1

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex system…

cs.HC2021

Topological Simplifications of Hypergraphs

Youjia Zhou, Archit Rathore, Emilie Purvine +1

We study hypergraph visualization via its topological simplification. We explore both vertex simplification and hyperedge simplification of hypergraphs using tools from topological…

cs.CL20201 cited

Argumentative Topology: Finding Loop(holes) in Logic

Sarah Tymochko, Zachary New, Lucius Bynum +4

Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the…

q-bio.QM2020

Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response

Song Feng, Emily Heath, Brett Jefferson +25

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular d…