
Novel View Synthesis via Depthguided Skip Connections
We introduce a principled approach for synthesizing new views of a scene...
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RealAnt: An OpenSource LowCost Quadruped for Research in RealWorld Reinforcement Learning
Current robot platforms available for research are either very expensive...
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Stationary Activations for Uncertainty Calibration in Deep Learning
We introduce a new family of nonlinear neural network activation functi...
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Movementinduced Priors for Deep Stereo
We propose a method for fusing stereo disparity estimation with movement...
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State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
We formulate approximate Bayesian inference in nonconjugate temporal an...
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Fast Variational Learning in StateSpace Gaussian Process Models
Gaussian process (GP) regression with 1D inputs can often be performed i...
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Movement Tracking by Optical Flow Assisted Inertial Navigation
Robust and accurate six degreeoffreedom tracking on portable devices r...
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Deep Residual Mixture Models
We propose Deep Residual Mixture Models (DRMMs) which share the many des...
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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful nonparametric probabilistic models for ...
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Deep Automodulators
We introduce a novel autoencoder model that deviates from traditional au...
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Gaussian Process Priors for ViewAware Inference
We derive a principled framework for encoding prior knowledge of informa...
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Scalable Exact Inference in MultiOutput Gaussian Processes
Multioutput Gaussian processes (MOGPs) leverage the flexibility and int...
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Iterative Path Reconstruction for LargeScale Inertial Navigation on Smartphones
Modern smartphones have all the sensing capabilities required for accura...
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MultiView Stereo by Temporal Nonparametric Fusion
We propose a novel idea for depth estimation from unstructured multivie...
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Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
We build on recent advances in progressively growing generative autoenco...
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Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Gaussian processes (GPs) provide a powerful framework for extrapolation,...
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Unstructured MultiView Depth Estimation Using MaskBased Multiplane Representation
This paper presents a novel method, MaskMVS, to solve depth estimation f...
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EndtoEnd Probabilistic Inference for Nonstationary Audio Analysis
A typical audio signal processing pipeline includes multiple disjoint an...
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InfiniteHorizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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Unifying Probabilistic Models for TimeFrequency Analysis
In audio signal processing, probabilistic timefrequency models have man...
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Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
Strapdown inertial navigation systems are sensitive to the quality of th...
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ADVIO: An authentic dataset for visualinertial odometry
The lack of realistic and open benchmarking datasets for pedestrian visu...
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Pioneer Networks: Progressively Growing Generative Autoencoder
We introduce a novel generative autoencoder network model that learns to...
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Robust GyroscopeAided Camera SelfCalibration
Camera calibration for estimating the intrinsic parameters and lens dist...
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Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
We present a method for scalable and fully 3D magnetic field simultaneou...
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Recursive Chaining of Reversible Imagetoimage Translators For Face Aging
This paper addresses the modeling and simulation of progressive changes ...
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State Space Gaussian Processes with NonGaussian Likelihood
We provide a comprehensive overview and tooling for GP modeling with non...
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PIVO: Probabilistic InertialVisual Odometry for OcclusionRobust Navigation
This paper presents a novel method for visualinertial odometry. The met...
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Inertial Odometry on Handheld Smartphones
Building a complete inertial navigation system using the limited quality...
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Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: t...
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Regularizing Solutions to the MEG Inverse Problem Using SpaceTime Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reco...
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Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoo...
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Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Gaussian processes allow for flexible specification of prior assumptions...
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Hilbert Space Methods for ReducedRank Gaussian Process Regression
This paper proposes a novel scheme for reducedrank Gaussian process reg...
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Arno Solin
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