1 code implementation • 4 Feb 2024 • Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
1 code implementation • 26 Sep 2023 • Mathilde Papillon, Mustafa Hajij, Helen Jenne, Johan Mathe, Audun Myers, Theodore Papamarkou, Tolga Birdal, Tamal Dey, Tim Doster, Tegan Emerson, Gurusankar Gopalakrishnan, Devendra Govil, Aldo Guzmán-Sáenz, Henry Kvinge, Neal Livesay, Soham Mukherjee, Shreyas N. Samaga, Karthikeyan Natesan Ramamurthy, Maneel Reddy Karri, Paul Rosen, Sophia Sanborn, Robin Walters, Jens Agerberg, Sadrodin Barikbin, Claudio Battiloro, Gleb Bazhenov, Guillermo Bernardez, Aiden Brent, Sergio Escalera, Simone Fiorellino, Dmitrii Gavrilev, Mohammed Hassanin, Paul Häusner, Odin Hoff Gardaa, Abdelwahed Khamis, Manuel Lecha, German Magai, Tatiana Malygina, Rubén Ballester, Kalyan Nadimpalli, Alexander Nikitin, Abraham Rabinowitz, Alessandro Salatiello, Simone Scardapane, Luca Scofano, Suraj Singh, Jens Sjölund, Pavel Snopov, Indro Spinelli, Lev Telyatnikov, Lucia Testa, Maosheng Yang, Yixiao Yue, Olga Zaghen, Ali Zia, Nina Miolane
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
no code implementations • 1 Aug 2023 • Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee, Paul Rosen, Danielle Albers Szafir, Jinwook Seo
In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity.
3 code implementations • 1 Jun 2022 • Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations.
no code implementations • 2 Aug 2021 • Zachariah J. Beasley, Les A. Piegl, Paul Rosen
Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial.
1 code implementation • 19 Jan 2021 • Alon Friedman, Paul Rosen
Based on the literature review from the field of Writing Studies, this paper proposes a new framework to implement visualization peer review in the classroom to engage today's students.
Human-Computer Interaction
1 code implementation • 27 Jul 2020 • Paul Rosen, Ghulam Jilani Quadri
We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks.
Human-Computer Interaction
no code implementations • 12 Feb 2020 • Mustafa Hajij, Elizabeth Munch, Paul Rosen
The PageRank of a graph is a scalar function defined on the node set of the graph which encodes nodes centrality information of the graph.
no code implementations • 3 Sep 2019 • Junyi Tu, Paul Rosen
Enhancement is an important step in post-processing digital images for personal use, in medical imaging, and for object recognition.
no code implementations • 21 Apr 2019 • Yunhao Zhang, Haowen Liu, Paul Rosen, Mustafa Hajij
We use persistent homology along with the eigenfunctions of the Laplacian to study similarity amongst triangulated 2-manifolds.
no code implementations • 18 Oct 2018 • Mustafa Hajij, Paul Rosen
That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure.
3 code implementations • 3 Apr 2018 • Paul Rosen, Mustafa Hajij, Bei Wang
Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints.
no code implementations • 11 Dec 2017 • Mustafa Hajij, Basem Assiri, Paul Rosen
The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees.
no code implementations • 24 Oct 2017 • Alejandro Robles, Mustafa Hajij, Paul Rosen
We study the topological construction called Mapper in the context of simply connected domains, in particular on images.