Search Results for author: Eric Maris

Found 11 papers, 1 papers with code

An internal sensory model allows for balance control based on non-actionable proprioceptive feedback

no code implementations1 Mar 2024 Eric Maris

All motor tasks with a mechanical system (a human body, a rider on a bicycle) that is approximately linear in the part of the state space where it stays most of the time (e. g., upright balance control) have the following property: actionable sensory feedback allows for optimal control actions that are a simple linear combination of the sensory feedback.

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

no code implementations23 Feb 2022 Eric Maris

This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC).

Wasserstein Variational Inference

no code implementations NeurIPS 2018 Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.

Bayesian Inference Variational Inference

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

1 code implementation19 May 2017 Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven, Eric Maris

In the Bayesian filtering example, we show that the method can be used to filter complex nonlinear and non-Gaussian signals defined on manifolds.

Density Estimation

GP CaKe: Effective brain connectivity with causal kernels

no code implementations NeurIPS 2017 Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris

Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.

Causal Inference

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis

no code implementations10 Apr 2017 Luca Ambrogioni, Eric Maris

This is possible because the posterior expectation of Gaussian process regression maps a finite set of samples to a function defined on the whole real line, expressed as a linear combination of covariance functions.

regression

Estimating Nonlinear Dynamics with the ConvNet Smoother

no code implementations17 Feb 2017 Luca Ambrogioni, Umut Güçlü, Eric Maris, Marcel van Gerven

Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.

Complex-valued Gaussian Process Regression for Time Series Analysis

no code implementations30 Nov 2016 Luca Ambrogioni, Eric Maris

Furthermore, the complex-valued Gaussian process regression allows to incorporate prior information about the structure in signal and noise and thereby to tailor the analysis to the features of the signal.

regression Time Series +1

Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes

no code implementations31 Oct 2016 Luca Ambrogioni, Eric Maris

In this paper, we introduce a new framework for analyzing nonstationary time series using locally stationary Gaussian process analysis with parameters that are coupled through a hidden Markov model.

Gaussian Processes Time Series +1

Dynamic Decomposition of Spatiotemporal Neural Signals

no code implementations9 May 2016 Luca Ambrogioni, Marcel A. J. van Gerven, Eric Maris

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks.

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