Fetching the paper…
Reading the bibliography…
In this work we take a Category Theoretic perspective on the relationship between probabilistic modeling and function approximation.
Bruno Gavranovic · 1907
Earlier work this paper cites.
The category of probabilistic mappings
F William Lawvere · 1962
Earlier work this paper cites.
Topics in Stochastic Processes
R.B. Ash, M.F. Gardner, and M.F. Gardner · 1975
Earlier work this paper cites.
A categorical approach to probability theory
Michele Giry · 1982
Earlier work this paper cites.
Probability and Measure
Patrick Billingsley · 1986
Earlier work this paper cites.
What is stochastic independence?
Uwe Franz · 2002
Earlier work this paper cites.
Conformal field theory as a nuclear functor
Richard Blute, Prakash Panangaden, and Dorette Pronk · 2007
Earlier work this paper cites.
Lévy processes, stable processes, and subordinators
Steven P Lalley · 2007
Cited alongside, same era.
A review of probability theory, 2010
Terence Tao · 2010
Cited alongside, same era.
Bayesian machine learning via category theory
Jared Culbertson and Kirk Sturtz · 2013
Cited alongside, same era.
A categorical foundation for Bayesian probability
Jared Culbertson and Kirk Sturtz · 2014
Cited alongside, same era.
How deep is the feature analysis underlying rapid visual categorization?
Sven Eberhardt, Jonah Cader, and Thomas Serre · 2016
Cited alongside, same era.
Malte Gerhold, Stephanie Lachs, and Michael Schürmann · 2016
Backprop as functor: A compositional perspective on supervised learning
Brendan Fong, David Spivak, and Rémy Tuyéras · 2019
Later among the works it cites.
A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics
Tobias Fritz · 2020
Closest in time.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
Closest in time.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Disintegration and Bayesian inversion via string diagrams
Kenta Cho and Bart Jacobs · 2019
Cited alongside, same era.
Edgar Y. Walker, R. James Cotton, Wei Ji Ma, and Andreas S. Tolias · 2020
Closest in time.
Towards foundations of categorical cybernetics, 2021
Matteo Capucci, Bruno Gavranović, Jules Hedges, and Eigil Fjeldgren Rischel · 2021
Closest in time.
Categorical foundations of gradient-based learning
G. S. H. Cruttwell, Bruno Gavranović, Neil Ghani, Paul Wilson, and Fabio Zanasi · 2021
Closest in time.