Linköping University

∙ Linköping , Sweden ∙ 0 followers

Linköping University (LiU) is known for innovative education, transnational research and close cooperation with industry and society.

Learning Video Instance Segmentation with Recurrent Graph Neural Networks

Most existing approaches to video instance segmentation comprise multipl...
12/07/2020 ∙ by Joakim Johnander, et al. ∙ 8 ∙ share

read it
Normalized Convolution Upsampling for Refined Optical Flow Estimation

Optical flow is a regression task where convolutional neural networks (C...
02/13/2021 ∙ by Abdelrahman Eldesokey, et al. ∙ 8 ∙ share

read it
Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning

Lattice-based motion planning is a hybrid planning method where a plan m...
05/15/2020 ∙ by Mattias Tiger, et al. ∙ 3 ∙ share

read it
Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Approximate inference in probabilistic graphical models (PGMs) can be gr...
01/08/2019 ∙ by Fredrik Lindsten, et al. ∙ 2 ∙ share

read it
Single-frame Regularization for Temporally Stable CNNs

Convolutional neural networks (CNNs) can model complicated non-linear re...
02/27/2019 ∙ by Gabriel Eilertsen, et al. ∙ 2 ∙ share

read it
Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

Many modern parallel computing systems are heterogeneous at their node l...
04/18/2017 ∙ by Suejb Memeti, et al. ∙ 0 ∙ share

read it
Unifying DAGs and UGs

We introduce a new class of graphical models that generalizes Lauritzen-...
08/29/2017 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
HDR image reconstruction from a single exposure using deep CNNs

Camera sensors can only capture a limited range of luminance simultaneou...
10/20/2017 ∙ by Gabriel Eilertsen, et al. ∙ 0 ∙ share

read it
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for ...
10/17/2017 ∙ by Apostolia Tsirikoglou, et al. ∙ 0 ∙ share

read it
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models

We introduce a new family of graphical models that consists of graphs wi...
12/22/2016 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Representing Independence Models with Elementary Triplets

In an independence model, the triplets that represent conditional indepe...
12/04/2016 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
High-dimensional Filtering using Nested Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods comprise one of the most successful...
12/29/2016 ∙ by Christian A. Naesseth, et al. ∙ 0 ∙ share

read it
Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences

Traditionally, pose estimation is considered as a two step problem. Firs...
03/15/2017 ∙ by Henry Bradler, et al. ∙ 0 ∙ share

read it
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs

We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the sem...
11/18/2015 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

Data-efficient reinforcement learning (RL) in continuous state-action sp...
10/08/2015 ∙ by John-Alexander M. Assael, et al. ∙ 0 ∙ share

read it
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression

Generating user interpretable multi-class predictions in data rich envir...
01/31/2016 ∙ by Måns Magnusson, et al. ∙ 0 ∙ share

read it
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables

Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Baye...
11/17/2015 ∙ by Johan Dahlin, et al. ∙ 0 ∙ share

read it
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family

In this paper, a Bayesian inference technique based on Taylor series app...
10/05/2015 ∙ by Tohid Ardeshiri, et al. ∙ 0 ∙ share

read it
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation

In this paper we introduce an efficient method to unwrap multi-frequency...
08/18/2016 ∙ by Felix Järemo Lawin, et al. ∙ 0 ∙ share

read it
Error AMP Chain Graphs

Any regular Gaussian probability distribution that can be represented by...
06/28/2013 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent per...
08/12/2016 ∙ by Martin Danelljan, et al. ∙ 0 ∙ share

read it
Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence

We propose a greedy mixture reduction algorithm which is capable of prun...
08/22/2015 ∙ by Tohid Ardeshiri, et al. ∙ 0 ∙ share

read it
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version

This paper deals with chain graphs under the Andersson-Madigan-Perlman (...
03/04/2013 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Approximate Counting of Graphical Models Via MCMC Revisited

In Peña (2007), MCMC sampling is applied to approximately calculate the ...
01/30/2013 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Quasi-Newton particle Metropolis-Hastings

Particle Metropolis-Hastings enables Bayesian parameter inference in gen...
02/12/2015 ∙ by Johan Dahlin, et al. ∙ 0 ∙ share

read it
Newton-based maximum likelihood estimation in nonlinear state space models

Maximum likelihood (ML) estimation using Newton's method in nonlinear st...
02/12/2015 ∙ by Manon Kok, et al. ∙ 0 ∙ share

read it
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo

Gaussian process regression is a popular method for non-parametric proba...
02/06/2015 ∙ by Andreas Svensson, et al. ∙ 0 ∙ share

read it
Parameterized Complexity and Kernel Bounds for Hard Planning Problems

The propositional planning problem is a notoriously difficult computatio...
11/02/2012 ∙ by Christer Bäckström, et al. ∙ 0 ∙ share

read it
Maximum Entropy Kernels for System Identification

A new nonparametric approach for system identification has been recently...
11/20/2014 ∙ by Francesca Paola Carli, et al. ∙ 0 ∙ share

read it
The Complexity of Planning Revisited - A Parameterized Analysis

The early classifications of the computational complexity of planning un...
08/13/2012 ∙ by Christer Baeckstroem, et al. ∙ 0 ∙ share

read it
Efficient Robust Mean Value Calculation of 1D Features

A robust mean value is often a good alternative to the standard mean val...
01/29/2016 ∙ by Erik Jonsson, et al. ∙ 0 ∙ share

read it
Learning AMP Chain Graphs under Faithfulness

This paper deals with chain graphs under the alternative Andersson-Madig...
04/24/2012 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Speeding Up MCMC by Efficient Data Subsampling

We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
04/16/2014 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it
Particle Gibbs with Ancestor Sampling

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combini...
01/03/2014 ∙ by Fredrik Lindsten, et al. ∙ 0 ∙ share

read it
Towards Optimal Learning of Chain Graphs

In this paper, we extend Meek's conjecture (Meek 1997) from directed and...
09/25/2011 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Every LWF and AMP chain graph originates from a set of causal models

This paper aims at justifying LWF and AMP chain graphs by showing that t...
12/10/2013 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Finding Consensus Bayesian Network Structures

Suppose that multiple experts (or learning algorithms) provide us with a...
01/10/2011 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

State-space models are successfully used in many areas of science, engin...
06/12/2013 ∙ by Roger Frigola, et al. ∙ 0 ∙ share

read it
Reading Dependencies from Covariance Graphs

The covariance graph (aka bi-directed graph) of a probability distributi...
10/21/2010 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
Hybrid Rules with Well-Founded Semantics

A general framework is proposed for integration of rules and external fi...
06/20/2009 ∙ by W. Drabent, et al. ∙ 0 ∙ share

read it
Ancestor Sampling for Particle Gibbs

We present a novel method in the family of particle MCMC methods that we...
10/25/2012 ∙ by Fredrik Lindsten, et al. ∙ 0 ∙ share

read it
3D planar patch extraction from stereo using probabilistic region growing

This article presents a novel 3D planar patch extraction method using a ...
06/27/2014 ∙ by Vasileios Zografos, et al. ∙ 0 ∙ share

read it
Saccadic Eye Movements and the Generalized Pareto Distribution

We describe a statistical analysis of the eye tracker measurements in a ...
06/24/2014 ∙ by Reiner Lenz, et al. ∙ 0 ∙ share

read it
Faithfulness in Chain Graphs: The Gaussian Case

This paper deals with chain graphs under the classic Lauritzen-Wermuth-F...
08/13/2010 ∙ by Jose M. Peña, et al. ∙ 0 ∙ share

read it
A Unified Framework for Multi-Sensor HDR Video Reconstruction

One of the most successful approaches to modern high quality HDR-video c...
08/22/2013 ∙ by Joel Kronander, et al. ∙ 0 ∙ share

read it
Sparse motion segmentation using multiple six-point consistencies

We present a method for segmenting an arbitrary number of moving objects...
12/09/2010 ∙ by Vasileios Zografos, et al. ∙ 0 ∙ share

read it
Efficient computational noise in GLSL

We present GLSL implementations of Perlin noise and Perlin simplex noise...
04/06/2012 ∙ by Ian McEwan, et al. ∙ 0 ∙ share

read it
On the Complexity of CCG Parsing

We study the parsing complexity of Combinatory Categorial Grammar (CCG) ...
02/21/2017 ∙ by Marco Kuhlmann, et al. ∙ 0 ∙ share

read it
Folding Polyominoes into (Poly)Cubes

We study the problem of folding a polyomino P into a polycube Q, allowin...
12/26/2017 ∙ by Oswin Aichholzer, et al. ∙ 0 ∙ share

read it
Bayesian uncertainty quantification in linear models for diffusion MRI

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue micr...
11/16/2017 ∙ by Jens Sjölund, et al. ∙ 0 ∙ share

read it

Linköping University

Featured Researchers