Search Results for author: Erik Bodin

Found 7 papers, 3 papers with code

Making Differentiable Architecture Search less local

no code implementations21 Apr 2021 Erik Bodin, Federico Tomasi, Zhenwen Dai

Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures.

Neural Architecture Search

Black-box density function estimation using recursive partitioning

1 code implementation26 Oct 2020 Erik Bodin, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.

Bayesian Inference

Compositional uncertainty in deep Gaussian processes

1 code implementation17 Sep 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.

Bayesian Inference Gaussian Processes +1

Modulating Surrogates for Bayesian Optimization

no code implementations ICML 2020 Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.

Bayesian Optimization Gaussian Processes

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

1 code implementation13 Dec 2018 Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell

The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.

Nonparametric Inference for Auto-Encoding Variational Bayes

no code implementations18 Dec 2017 Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell

We would like to learn latent representations that are low-dimensional and highly interpretable.

Latent Gaussian Process Regression

no code implementations18 Jul 2017 Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.


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