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https://april-tools.github.io/publications/loconte2023subtractive

Subtractive Mixture Models via Squaring: Representation and Learning

APRIL Lab in Edinburgh, We design probabilistic ML systems that are provably reliable in the #wild by combining complex reasoning with efficient inference and learning.



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Subtractive Mixture Models via Squaring: Representation and Learning

https://april-tools.github.io/publications/loconte2023subtractive

APRIL Lab in Edinburgh, We design probabilistic ML systems that are provably reliable in the #wild by combining complex reasoning with efficient inference and learning.



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https://april-tools.github.io/publications/loconte2023subtractive

Subtractive Mixture Models via Squaring: Representation and Learning

APRIL Lab in Edinburgh, We design probabilistic ML systems that are provably reliable in the #wild by combining complex reasoning with efficient inference and learning.

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      Subtractive Mixture Models via Squaring: Representation and Learning - april Lab
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    • article:published_time
      2024-01-16T00:00:00-08:00
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      Subtractive Mixture Models via Squaring: Representation and Learning
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      https://github.com/pages/april-tools/publications/loconte2023subtractive
    • og:description
      We propose to build (deep) subtractive mixture models by squaring circuits. We theoretically prove their expressiveness by deriving an exponential lowerbound on the size of circuits with positive parameters only.

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    • headline
      Subtractive Mixture Models via Squaring: Representation and Learning
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      We propose to build (deep) subtractive mixture models by squaring circuits. We theoretically prove their expressiveness by deriving an exponential lowerbound on the size of circuits with positive parameters only.
    • datePublished
      January 16, 2024

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