Search Results for author: Arno Solin

Found 47 papers, 33 papers with code

Non-separable Spatio-temporal Graph Kernels via SPDEs

no code implementations16 Nov 2021 Alexander Nikitin, ST John, Arno Solin, Samuel Kaski

Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs.

Gaussian Processes

Dual Parameterization of Sparse Variational Gaussian Processes

1 code implementation NeurIPS 2021 Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin

Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits.

Gaussian Processes

Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

1 code implementation2 Nov 2021 William J. Wilkinson, Simo Särkkä, Arno Solin

We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the parameters of a Bayesian posterior distribution.

Bayesian Inference Gaussian Processes +1

Spatio-Temporal Variational Gaussian Processes

1 code implementation NeurIPS 2021 Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi, Arno Solin, Theodoros Damoulas

We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to time.

Gaussian Processes Variational Inference

Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation

1 code implementation NeurIPS 2021 Arno Solin, Ella Tamir, Prakhar Verma

Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning.

Periodic Activation Functions Induce Stationarity

2 code implementations NeurIPS 2021 Lassi Meronen, Martin Trapp, Arno Solin

Neural network models are known to reinforce hidden data biases, making them unreliable and difficult to interpret.


Independent Component Alignment for Multi-task Learning

no code implementations29 Sep 2021 Dmitry Senushkin, Iaroslav Melekhov, Mikhail Romanov, Anton Konushin, Juho Kannala, Arno Solin

We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective.

Camera Relocalization Multi-Label Image Classification +2

Sparse Gaussian Processes for Stochastic Differential Equations

no code implementations NeurIPS Workshop DLDE 2021 Prakhar Verma, Vincent Adam, Arno Solin

We frame the problem of learning stochastic differential equations (SDEs) from noisy observations as an inference problem and aim to maximize the marginal likelihood of the observations in a joint model of the latent paths and the noisy observations.

Frame Gaussian Processes +1

HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry

1 code implementation22 Jun 2021 Otto Seiskari, Pekka Rantalankila, Juho Kannala, Jerry Ylilammi, Esa Rahtu, Arno Solin

We present HybVIO, a novel hybrid approach for combining filtering-based visual-inertial odometry (VIO) with optimization-based SLAM.

Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes

1 code implementation pproximateinference AABI Symposium 2021 Will Tebbutt, Arno Solin, Richard E. Turner

Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data.

Epidemiology Gaussian Processes +1

Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation

1 code implementation NeurIPS 2021 Arno Solin, Ella Maija Tamir, Prakhar Verma

Simulation-based techniques such as variants of stochastic Runge–Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning.

Sparse Algorithms for Markovian Gaussian Processes

1 code implementation19 Mar 2021 William J. Wilkinson, Arno Solin, Vincent Adam

Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series.

Bayesian Inference Gaussian Processes +2

Novel View Synthesis via Depth-guided Skip Connections

1 code implementation5 Jan 2021 Yuxin Hou, Arno Solin, Juho Kannala

Flow predictions enable the target view to re-use pixels directly, but can easily lead to distorted results.

Novel View Synthesis

RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning

1 code implementation5 Nov 2020 Rinu Boney, Jussi Sainio, Mikko Kaivola, Arno Solin, Juho Kannala

We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks.


Stationary Activations for Uncertainty Calibration in Deep Learning

1 code implementation NeurIPS 2020 Lassi Meronen, Christabella Irwanto, Arno Solin

We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Mat\'ern family of kernels in Gaussian process (GP) models.

General Classification

Movement-induced Priors for Deep Stereo

1 code implementation18 Oct 2020 Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala, Arno Solin

We propose a method for fusing stereo disparity estimation with movement-induced prior information.

Disparity Estimation Frame +1

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

1 code implementation ICML 2020 William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin

EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework.

Bayesian Inference Gaussian Processes +1

Fast Variational Learning in State-Space Gaussian Process Models

1 code implementation9 Jul 2020 Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin

Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation.

Time Series Variational Inference

Movement Tracking by Optical Flow Assisted Inertial Navigation

no code implementations24 Jun 2020 Lassi Meronen, William J. Wilkinson, Arno Solin

We consider a visually dense approach, where the IMU data is fused with the dense optical flow field estimated from the camera data.

Optical Flow Estimation Probabilistic Deep Learning

Deep Residual Mixture Models

1 code implementation22 Jun 2020 Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo, Arno Solin

We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture.

Deep Automodulators

2 code implementations NeurIPS 2020 Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin

These networks can faithfully reproduce individual real-world input images like regular autoencoders, but also generate a fused sample from an arbitrary combination of several such images, allowing instantaneous 'style-mixing' and other new applications.


Gaussian Process Priors for View-Aware Inference

no code implementations6 Dec 2019 Yuxin Hou, Ari Heljakka, Arno Solin

While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention.

Frame Novel View Synthesis +1

Scalable Exact Inference in Multi-Output Gaussian Processes

no code implementations ICML 2020 Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling.

Gaussian Processes

Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes

no code implementations pproximateinference AABI Symposium 2019 William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin

The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior.

Bayesian Inference Gaussian Processes +1

Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones

no code implementations2 Jun 2019 Santiago Cortés Reina, Yuxin Hou, Juho Kannala, Arno Solin

Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking.

Motion Estimation

Multi-View Stereo by Temporal Nonparametric Fusion

1 code implementation ICCV 2019 Yuxin Hou, Juho Kannala, Arno Solin

The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing information from previous views.

Depth Estimation Disparity Estimation

Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders

1 code implementation12 Apr 2019 Ari Heljakka, Arno Solin, Juho Kannala

retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs.

Disentanglement Image Manipulation

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

1 code implementation10 Apr 2019 Arno Solin, Manon Kok

Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification.

Gaussian Processes General Classification

Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation

1 code implementation6 Feb 2019 Yuxin Hou, Arno Solin, Juho Kannala

This paper presents a novel method, MaskMVS, to solve depth estimation for unstructured multi-view image-pose pairs.

Depth Estimation

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

1 code implementation31 Jan 2019 William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.

Audio Signal Processing

Infinite-Horizon Gaussian Processes

1 code implementation NeurIPS 2018 Arno Solin, James Hensman, Richard E. Turner

The complexity is still cubic in the state dimension $m$ which is an impediment to practical application.

Gaussian Processes online learning

Unifying Probabilistic Models for Time-Frequency Analysis

1 code implementation6 Nov 2018 William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin

In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.

Audio Signal Processing Gaussian Processes +1

Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

1 code implementation10 Aug 2018 Santiago Cortés, Arno Solin, Juho Kannala

Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope.

ADVIO: An authentic dataset for visual-inertial odometry

1 code implementation ECCV 2018 Santiago Cortés, Arno Solin, Esa Rahtu, Juho Kannala

The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods.

Pioneer Networks: Progressively Growing Generative Autoencoder

1 code implementation9 Jul 2018 Ari Heljakka, Arno Solin, Juho Kannala

Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achieves high-quality reconstruction with $128{\times}128$ images without requiring a GAN discriminator.

Image Generation

Robust Gyroscope-Aided Camera Self-Calibration

1 code implementation31 May 2018 Santiago Cortés Reina, Arno Solin, Juho Kannala

This application paper proposes a model for estimating the parameters on the fly by fusing gyroscope and camera data, both readily available in modern day smartphones.

Camera Calibration Video Stabilization

Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps

no code implementations5 Apr 2018 Manon Kok, Arno Solin

We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information.

Recursive Chaining of Reversible Image-to-image Translators For Face Aging

2 code implementations14 Feb 2018 Ari Heljakka, Arno Solin, Juho Kannala

By treating the age phases as a sequence of image domains, we construct a chain of transformers that map images from one age domain to the next.


State Space Gaussian Processes with Non-Gaussian Likelihood

no code implementations ICML 2018 Hannes Nickisch, Arno Solin, Alexander Grigorievskiy

We provide a comprehensive overview and tooling for GP modeling with non-Gaussian likelihoods using state space methods.

Gaussian Processes

Inertial Odometry on Handheld Smartphones

1 code implementation1 Mar 2017 Arno Solin, Santiago Cortes, Esa Rahtu, Juho Kannala

Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible.

Variational Fourier features for Gaussian processes

1 code implementation21 Nov 2016 James Hensman, Nicolas Durrande, Arno Solin

This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes.

Gaussian Processes

Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions

no code implementations17 Apr 2016 Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä

We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

no code implementations7 Jun 2015 Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön

We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure.

Gaussian Processes

Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems

1 code implementation23 Apr 2015 Juho Kokkala, Arno Solin, Simo Särkkä

We consider approximate maximum likelihood parameter estimation in nonlinear state-space models.

Methodology Dynamical Systems Optimization and Control Computation

Hilbert Space Methods for Reduced-Rank Gaussian Process Regression

2 code implementations21 Jan 2014 Arno Solin, Simo Särkkä

On this approximate eigenbasis the eigenvalues of the covariance function can be expressed as simple functions of the spectral density of the Gaussian process, which allows the GP inference to be solved under a computational cost scaling as $\mathcal{O}(nm^2)$ (initial) and $\mathcal{O}(m^3)$ (hyperparameter learning) with $m$ basis functions and $n$ data points.

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