Topological Deep Learning (TDL) is gaining traction for its ability to capture higher-order interactions beyond the pairwise structure of #graphs, using tools from #algebraic #topology, especially combinatorial topological spaces.
How combinatorial topological spaces can be used to promote a paradigm shift from inferring pairwise to multiway latent relationships in data.
Several problems in machine learning call for methods able to infer and exploit multi-way, higher-order relationships hidden in the data. We propose the new beyond-graph paradigm of Latent Topology Inference, which aims to learn latent higher-order combinatorial topological spaces describing multi-way interactions among data points. To make Latent Topology Inference implementable, we introduce the Differentiable Cell Complex Module, a novel learnable function able to infer a latent cell complex to improve the downstream task.
