Search Results for author:
Found 7 papers, 4 papers with code
idtracker.ai: Tracking all individuals in large collectives of unmarked animals
Our understanding of collective animal behavior is limited by our ability to track each of the individuals.
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 group equivariance in the machine learning context.
Beyond topological persistence: Starting from networks
Nowadays, data generation, representation and analysis occupy central roles in human society.
Combinatorics Category Theory
Parametric machines: a fresh approach to architecture search
Using tools from topology and functional analysis, we provide a framework where artificial neural networks, and their architectures, can be formally described.
Machines of finite depth: towards a formalization of neural networks
We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction--machines of finite depth.
Neural network layers as parametric spans
Properties such as composability and automatic differentiation made artificial neural networks a pervasive tool in applications.
Persistence-based operators in machine learning
Artificial neural networks can learn complex, salient data features to achieve a given task.
