# VFunc: a Deep Generative Model for Functions

arXiv: https://arxiv.org/abs/1807.04106 · HTML: https://mlsanity.com/paper/1807.04106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation](https://mlsanity.com/paper/2105.00397/) (2021)

## Abstract (arXiv)

We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and parameter noise. In this short paper we describe our model, situate it in the context of prior work, and present proof-of-concept experiments for regression and RL.