
Reverse derivative categories
The reverse derivative is a fundamental operation in machine learning an...
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Categorical Foundations of GradientBased Learning
We propose a categorical foundation of gradientbased machine learning a...
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Mapping finite state machines to zkSNARKS Using Category Theory
We provide a categorical procedure to turn graphs corresponding to state...
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Categorical semantics of a simple differential programming language
With the increased interest in machine learning, and deep learning in pa...
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Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits....
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On The Reasons Behind Decisions
Recent work has shown that some common machine learning classifiers can ...
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The differential calculus of causal functions
Causal functions of sequences occur throughout computer science, from th...
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Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of socalled reverse differential categories. It can be used to learn the parameters of models which are expressed as morphisms of such categories. Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories. Note our methodology allows us to learn the parameters of boolean circuits directly, in contrast to existing binarised neural network approaches. Moreover, we demonstrate its empirical value by giving experimental results on benchmark machine learning datasets.
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