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Found 7 papers, 1 papers with code
Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Stochastic neural networks with discrete random variables are an important class of models for their expressiveness and interpretability.
Mechanistic Neural Networks for Scientific Machine Learning
This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences.
Differentiable Mathematical Programming for Object-Centric Representation Learning
To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation.
Stability Regularization for Discrete Representation Learning
Stability regularization is method to make the output of continuous functions of Gaussian random variables close to discrete, that is binary or categorical, without the need for significant manual tuning.
Variance Reduction in Hierarchical Variational Autoencoders
Variational autoencoders with deep hierarchies of stochastic layers have been known to suffer from the problem of posterior collapse, where the top layers fall back to the prior and become independent of input.
Low Bias Gradient Estimates for Very Deep Boolean Stochastic Networks
In this work we focus on stochastic networks with multiple layers of Boolean latent variables.
A Fourier View of REINFORCE
We show a connection between the Fourier spectrum of Boolean functions and the REINFORCE gradient estimator for binary latent variable models.
