27 citations · 28 across the 3 of their papers we have counts for
4 papers
GEFL: Extended Filtration Learning for Graph Classification
Simon Zhang, Soham Mukherjee, Tamal K. Dey
Extended persistence is a technique from topological data analysis to obtain global multiscale topological information from a graph. This includes information about connected compo…
D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
Soham Mukherjee, Shreyas N. Samaga, Cheng Xin +2
End-to-end topological learning using 1-parameter persistence is well-known. We show that the framework can be enhanced using 2-parameter persistence by adopting a recently introdu…
GRIL: A -parameter Persistence Based Vectorization for Machine Learning
Cheng Xin, Soham Mukherjee, Shreyas N. Samaga +1
-parameter persistent homology, a cornerstone in Topological Data Analysis (TDA), studies the evolution of topological features such as connected components and cycles hidden in…
Topological Deep Learning: Going Beyond Graph Data
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou +11
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…