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Found 9 papers, 3 papers with code
String Diagrams with Factorized Densities
A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models.
Computing with Categories in Machine Learning
Category theory has been successfully applied in various domains of science, shedding light on universal principles unifying diverse phenomena and thereby enabling knowledge transfer between them.
Deriving time-averaged active inference from control principles
Active inference offers a principled account of behavior as minimizing average sensory surprise over time.
A Probabilistic Generative Model of Free Categories
Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages.
Learning Proposals for Probabilistic Programs with Inference Combinators
Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives.
Learning a Deep Generative Model like a Program: the Free Category Prior
Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts.
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling.
Neural Topographic Factor Analysis for fMRI Data
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli.
Composing Modeling and Inference Operations with Probabilistic Program Combinators
We assume that models are dynamic, but that model composition is static, in the sense that combinator application takes place prior to evaluating the model on data.
