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We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories.
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Cartesian differential categories revisited
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Lenses, fibrations and universal translations
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On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 28) , Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 1139–1147
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Generative Adversarial Nets
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Backprop as Functor: A compositional perspective on supervised learning
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Diagrammatic Semantics for Digital Circuits
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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The simple essence of automatic differentiation (Differentiable functional programming made easy)
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Explainable AI: the Basics - Policy Briefing
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Reverse derivative categories. In CSL
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Dioptics: a Common Generalization of Open Games and Gradient-Based Learners
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Bruno Gavranovic. 2019 · 2019
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Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
Paul Wilson and Fabio Zanasi. 2020 · 2020
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Towards foundations of categorical cybernetics
Matteo Capucci, Bruno Gavranovi’c, Jules Hedges, and E. F. Rischel. 2021 · 2021
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Translating Extensive Form Games to Open Games with Agency
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