Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Maris, Eric"'
Autor:
Maris, Eric
Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the
Externí odkaz:
http://arxiv.org/abs/2403.00951
Autor:
Maris, Eric
Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicyc
Externí odkaz:
http://arxiv.org/abs/2202.11480
Autor:
Fries, Pascal, Maris, Eric
The field of in-vivo neurophysiology currently uses statistical standards that are based on tradition rather than formal analysis. Typically, data from two (or few) animals are pooled for one statistical test, or a significant test in a first animal
Externí odkaz:
http://arxiv.org/abs/2106.14562
Autor:
Ambrogioni, Luca, Güçlü, Umut, Berezutskaya, Julia, Borne, Eva W. P. van den, Güçlütürk, Yağmur, Hinne, Max, Maris, Eric, van Gerven, Marcel A. J.
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient ca
Externí odkaz:
http://arxiv.org/abs/1805.11542
Autor:
Ambrogioni, Luca, Güçlü, Umut, Güçlütürk, Yağmur, Hinne, Max, Maris, Eric, van Gerven, Marcel A. J.
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasse
Externí odkaz:
http://arxiv.org/abs/1805.11284
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel fu
Externí odkaz:
http://arxiv.org/abs/1705.07111
A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations a
Externí odkaz:
http://arxiv.org/abs/1705.05603
Autor:
Ambrogioni, Luca, Maris, Eric
Computing accurate estimates of the Fourier transform of analog signals from discrete data points is important in many fields of science and engineering. The conventional approach of performing the discrete Fourier transform of the data implicitly as
Externí odkaz:
http://arxiv.org/abs/1704.02828
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering. The most well-known method, the Kalman smoother (and the related Kalman filter), relies on assumptions o
Externí odkaz:
http://arxiv.org/abs/1702.05243
Autor:
Ambrogioni, Luca, Maris, Eric
The construction of synthetic complex-valued signals from real-valued observations is an important step in many time series analysis techniques. The most widely used approach is based on the Hilbert transform, which maps the real-valued signal into i
Externí odkaz:
http://arxiv.org/abs/1611.10073