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12/28/2022
Persistence-based operators in machine learning
Artificial neural networks can learn complex, salient data features to a...
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08/01/2022
Neural network layers as parametric spans
Properties such as composability and automatic differentiation made arti...
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04/27/2022
Machines of finite depth: towards a formalization of neural networks
We provide a unifying framework where artificial neural networks and the...
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09/15/2020
Steady and ranging sets in graph persistence
Generalised persistence functions (gp-functions) are defined on (ℝ, ≤)-i...
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07/06/2020
Parametric machines: a fresh approach to architecture search
Using tools from category theory, we provide a framework where artificia...
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05/22/2019
Rank-based persistence
Persistence has proved to be a valuable tool to analyze real world data ...
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12/31/2018
Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning
The aim of this paper is to provide a general mathematical framework for...
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03/12/2018
