Michael Riis Andersen | DeepAI

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∙ 04/08/2023

Polygonizer: An auto-regressive building delineator

In geospatial planning, it is often essential to represent objects in a ...

0 Maxim Khomiakov, et al. ∙

∙ 03/20/2023

Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery

We present a novel pipeline for learning the conditional distribution of...

0 Maxim Khomiakov, et al. ∙

∙ 01/14/2023

On the role of Model Uncertainties in Bayesian Optimization

Bayesian optimization (BO) is a popular method for black-box optimizatio...

0 Jonathan Foldager, et al. ∙

∙ 12/02/2022

SolarDK: A high-resolution urban solar panel image classification and localization dataset

The body of research on classification of solar panel arrays from aerial...

0 Maxim Khomiakov, et al. ∙

∙ 03/29/2022

Robust, Automated, and Accurate Black-box Variational Inference

Black-box variational inference (BBVI) now sees widespread use in machin...

0 Manushi Welandawe, et al. ∙

∙ 03/01/2021

Challenges and Opportunities in High-dimensional Variational Inference

We explore the limitations of and best practices for using black-box var...

17 Akash Kumar Dhaka, et al. ∙

∙ 09/01/2020

Robust, Accurate Stochastic Optimization for Variational Inference

We consider the problem of fitting variational posterior approximations ...

8 Akash Kumar Dhaka, et al. ∙

∙ 07/12/2020

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

We formulate approximate Bayesian inference in non-conjugate temporal an...

0 William J. Wilkinson, et al. ∙

∙ 03/25/2020

Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedba...

14 Eero Siivola, et al. ∙

∙ 01/03/2020

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Recently, new methods for model assessment, based on subsampling and pos...

0 Måns Magnusson, et al. ∙

∙ 11/07/2019

Gaussian process with derivative information for the analysis of the sunlight adverse effects on color of rock art paintings

Microfading Spectrometry (MFS) is a method for assessing light sensitivi...

0 Gabriel Riutort-Mayol, et al. ∙

∙ 10/17/2019

Ranking variables and interactions using predictive uncertainty measures

For complex nonlinear supervised learning models, assessing the relevanc...

29 Topi Paananen, et al. ∙

∙ 04/24/2019

Bayesian leave-one-out cross-validation for large data

Model inference, such as model comparison, model checking, and model sel...

20 Måns Magnusson, et al. ∙

∙ 01/31/2019

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

A typical audio signal processing pipeline includes multiple disjoint an...

0 William J. Wilkinson, et al. ∙

∙ 11/06/2018

Unifying Probabilistic Models for Time-Frequency Analysis

In audio signal processing, probabilistic time-frequency models have man...

0 William J. Wilkinson, et al. ∙

∙ 12/21/2017

Model selection for Gaussian processes utilizing sensitivity of posterior predictive distribution

We propose two novel methods for simplifying Gaussian process (GP) model...

0 Topi Paananen, et al. ∙

∙ 09/15/2015

Bayesian inference for spatio-temporal spike-and-slab priors

In this work, we address the problem of solving a series of underdetermi...

0 Michael Riis Andersen, et al. ∙

∙ 08/19/2015

Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems

We are interested in solving the multiple measurement vector (MMV) probl...

0 Michael Riis Andersen, et al. ∙

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