1 code implementation • 13 Mar 2024 • Bruno Gavranović
Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation.
no code implementations • 23 Feb 2024 • Bruno Gavranović, Paul Lessard, Andrew Dudzik, Tamara von Glehn, João G. M. Araújo, Petar Veličković
We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures.
no code implementations • 1 Dec 2022 • Bruno Gavranović, Mattia Villani
We define the bicategory of Graph Convolutional Neural Networks $\mathbf{GCNN}_n$ for an arbitrary graph with $n$ nodes.
no code implementations • 19 Sep 2022 • Bruno Gavranović
We establish a conjecture that the well-known isomorphism between cartesian lenses and optics arises out of the lax 2-adjunction between their double-categorical counterparts.
no code implementations • 13 Jun 2021 • Dan Shiebler, Bruno Gavranović, Paul Wilson
Over the past two decades machine learning has permeated almost every realm of technology.
no code implementations • 2 Mar 2021 • G. S. H. Cruttwell, Bruno Gavranović, Neil Ghani, Paul Wilson, Fabio Zanasi
We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories.
no code implementations • 15 Sep 2020 • Bruno Gavranović
Neural networks are a general framework for differentiable optimization which includes many other machine learning approaches as special cases.
no code implementations • 16 Jul 2019 • Bruno Gavranović
Neural networks have become an increasingly popular tool for solving many real-world problems.