
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Most existing approaches to video instance segmentation comprise multipl...
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Normalized Convolution Upsampling for Refined Optical Flow Estimation
Optical flow is a regression task where convolutional neural networks (C...
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Enhancing Latticebased Motion Planning with Introspective Learning and Reasoning
Latticebased motion planning is a hybrid planning method where a plan m...
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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
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Singleframe Regularization for Temporally Stable CNNs
Convolutional neural networks (CNNs) can model complicated nonlinear re...
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Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption
Many modern parallel computing systems are heterogeneous at their node l...
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Unifying DAGs and UGs
We introduce a new class of graphical models that generalizes Lauritzen...
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HDR image reconstruction from a single exposure using deep CNNs
Camera sensors can only capture a limited range of luminance simultaneou...
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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 ...
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Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
We introduce a new family of graphical models that consists of graphs wi...
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Representing Independence Models with Elementary Triplets
In an independence model, the triplets that represent conditional indepe...
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Highdimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful...
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Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences
Traditionally, pose estimation is considered as a two step problem. Firs...
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Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
We extend AnderssonMadiganPerlman chain graphs by (i) relaxing the sem...
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DataEfficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Dataefficient reinforcement learning (RL) in continuous stateaction sp...
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DOLDA  a regularized supervised topic model for highdimensional multiclass regression
Generating user interpretable multiclass predictions in data rich envir...
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Accelerating pseudomarginal MetropolisHastings by correlating auxiliary variables
Pseudomarginal MetropolisHastings (pmMH) is a powerful method for Baye...
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Bayesian Inference via Approximation of Loglikelihood for Priors in Exponential Family
In this paper, a Bayesian inference technique based on Taylor series app...
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Efficient MultiFrequency Phase Unwrapping using Kernel Density Estimation
In this paper we introduce an efficient method to unwrap multifrequency...
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Error AMP Chain Graphs
Any regular Gaussian probability distribution that can be represented by...
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Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent per...
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Gaussian Mixture Reduction Using Reverse KullbackLeibler Divergence
We propose a greedy mixture reduction algorithm which is capable of prun...
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Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
This paper deals with chain graphs under the AnderssonMadiganPerlman (...
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Approximate Counting of Graphical Models Via MCMC Revisited
In Peña (2007), MCMC sampling is applied to approximately calculate the ...
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QuasiNewton particle MetropolisHastings
Particle MetropolisHastings enables Bayesian parameter inference in gen...
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Newtonbased maximum likelihood estimation in nonlinear state space models
Maximum likelihood (ML) estimation using Newton's method in nonlinear st...
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Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Gaussian process regression is a popular method for nonparametric proba...
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Parameterized Complexity and Kernel Bounds for Hard Planning Problems
The propositional planning problem is a notoriously difficult computatio...
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Maximum Entropy Kernels for System Identification
A new nonparametric approach for system identification has been recently...
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The Complexity of Planning Revisited  A Parameterized Analysis
The early classifications of the computational complexity of planning un...
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Efficient Robust Mean Value Calculation of 1D Features
A robust mean value is often a good alternative to the standard mean val...
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Learning AMP Chain Graphs under Faithfulness
This paper deals with chain graphs under the alternative AnderssonMadig...
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Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
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Particle Gibbs with Ancestor Sampling
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combini...
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Towards Optimal Learning of Chain Graphs
In this paper, we extend Meek's conjecture (Meek 1997) from directed and...
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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...
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Finding Consensus Bayesian Network Structures
Suppose that multiple experts (or learning algorithms) provide us with a...
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Bayesian Inference and Learning in Gaussian Process StateSpace Models with Particle MCMC
Statespace models are successfully used in many areas of science, engin...
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Reading Dependencies from Covariance Graphs
The covariance graph (aka bidirected graph) of a probability distributi...
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Hybrid Rules with WellFounded Semantics
A general framework is proposed for integration of rules and external fi...
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Ancestor Sampling for Particle Gibbs
We present a novel method in the family of particle MCMC methods that we...
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3D planar patch extraction from stereo using probabilistic region growing
This article presents a novel 3D planar patch extraction method using a ...
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Saccadic Eye Movements and the Generalized Pareto Distribution
We describe a statistical analysis of the eye tracker measurements in a ...
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Faithfulness in Chain Graphs: The Gaussian Case
This paper deals with chain graphs under the classic LauritzenWermuthF...
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A Unified Framework for MultiSensor HDR Video Reconstruction
One of the most successful approaches to modern high quality HDRvideo c...
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Sparse motion segmentation using multiple sixpoint consistencies
We present a method for segmenting an arbitrary number of moving objects...
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Efficient computational noise in GLSL
We present GLSL implementations of Perlin noise and Perlin simplex noise...
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On the Complexity of CCG Parsing
We study the parsing complexity of Combinatory Categorial Grammar (CCG) ...
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Folding Polyominoes into (Poly)Cubes
We study the problem of folding a polyomino P into a polycube Q, allowin...
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Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue micr...
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