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Found 9 papers, 6 papers with code
Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking
Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph.
GRIL: A $2$-parameter Persistence Based Vectorization for Machine Learning
We show that this vector representation is $1$-Lipschitz stable and differentiable with respect to underlying filtration functions and can be easily integrated into machine learning models to augment encoding topological features.
Topological structure of complex predictions
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data.
Topological Deep Learning: Going Beyond Graph Data
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.
Determining clinically relevant features in cytometry data using persistent homology
This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.
Computing Zigzag Persistence on Graphs in Near-Linear Time
Specifically, given a filtration with $m$ additions and deletions on a graph with $n$ vertices and edges, the algorithm for $0$-dimension runs in $O(m\log^2 n+m\log m)$ time and the algorithm for 1-dimension runs in $O(m\log^4 n)$ time.
Computational Geometry Algebraic Topology
Road Network Reconstruction from Satellite Images with Machine Learning Supported by Topological Methods
Next, in a fully automatic framework, we leverage the power of the discrete-Morse based graph reconstruction algorithm to train a CNN from a collection of images without labelled data and use the same algorithm to produce the final output from the segmented images created by the trained CNN.
Filtration Simplification for Persistent Homology via Edge Contraction
Persistent homology is a popular data analysis technique that is used to capture the changing topology of a filtration associated with some simplicial complex $K$.
Computational Geometry Algebraic Topology
Graph Reconstruction by Discrete Morse Theory
Specifically, first, leveraging existing theoretical understanding of persistence-guided discrete Morse cancellation, we provide a simplified version of the existing discrete Morse-based graph reconstruction algorithm.
Computational Geometry
