Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Urs Köster"'
Autor:
Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs Köster, Rif A. Saurous, Matthew Hoffman
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As the scale of modern datasets increases,
Externí odkaz:
https://doaj.org/article/9ebb0aa96c0a4bf1a703d75d1b8e62b6
Publikováno v:
PLoS Computational Biology, Vol 10, Iss 7, p e1003684 (2014)
We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations betwee
Externí odkaz:
https://doaj.org/article/f5f91a2439da44bebde70785a62bacdd
Publikováno v:
ACSSC
A fundamental challenge in Neuroscience is to infer the emergent properties of networks of neurons. Our current understanding of neural processing is largely based on the response properties of single cells, but techniques to simultaneously record ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74a05d6a46dc625bca23c931e8b979d5
https://doi.org/10.1109/acssc.2013.6810295
https://doi.org/10.1109/acssc.2013.6810295
Autor:
Aapo Hyvärinen, Urs Köster
Publikováno v:
Neural computation. 22(9)
We consider a hierarchical two-layer model of natural signals in which both layers are learned from the data. Estimation is accomplished by score matching, a recently proposed estimation principle for energy-based models. If the first-layer outputs a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db63dbc3191ee48a589eecd9992b2d2b
https://pubmed.ncbi.nlm.nih.gov/20569179
https://pubmed.ncbi.nlm.nih.gov/20569179
Autor:
Aapo Hyvärinen, Urs Köster
Publikováno v:
2009 International Workshop on Local and Non-Local Approximation in Image Processing.
Score matching is recent framework for parameter estimation in energy based models. In this class of probabilistic models, the intractable partition function has classically made learning a computationally expensive process necessitating the use of a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8882d02f5d2280d47ef23fde01365d37
https://doi.org/10.1109/lnla.2009.5278409
https://doi.org/10.1109/lnla.2009.5278409
Publikováno v:
Independent Component Analysis and Signal Separation ISBN: 9783642005985
ICA
ICA
We present a novel extension to Independent Component Analysis (ICA), where the data is generated as the product of two submodels, each of which follow an ICA model, and which combine in a horizontal fashion. This is in contrast to previous nonlinear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::38b4b449460d690afee3480241635ffa
https://doi.org/10.1007/978-3-642-00599-2_64
https://doi.org/10.1007/978-3-642-00599-2_64
Autor:
Aapo Hyvärinen, Urs Köster
Publikováno v:
Network (Bristol, England). 18(2)
In previous work, we presented a statistical model of natural images that produced outputs similar to receptive fields of complex cells in primary visual cortex. However, a weakness of that model was that the structure of the pooling was assumed a pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d7e05eb9198f8e01d228595e458d07c
https://pubmed.ncbi.nlm.nih.gov/17852755
https://pubmed.ncbi.nlm.nih.gov/17852755
Autor:
Aapo Hyvärinen, Urs Köster
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540746935
ICANN (2)
University of Helsinki
ICANN (2)
University of Helsinki
Capturing regularities in high-dimensional data is an important problem in machine learning and signal processing. Here we present a statistical model that learns a nonlinear representation from the data that reflects abstract, invariant properties o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2a850c4f0a4fe80155e24eccc8da9ad
https://doi.org/10.1007/978-3-540-74695-9_82
https://doi.org/10.1007/978-3-540-74695-9_82
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 10, Iss 7, p e1003684 (2014)
PLoS Computational Biology, Vol 10, Iss 7, p e1003684 (2014)
We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations betwee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9852836c7097b102f1ed7e081b2a517c
https://doi.org/10.1371/journal.pcbi.1003684
https://doi.org/10.1371/journal.pcbi.1003684
Publikováno v:
University of Helsinki
Independent Component Analysis and Signal Separation ISBN: 9783642005985
ICA
Independent Component Analysis and Signal Separation ISBN: 9783642005985
ICA
Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a principled model for whole images, unlike ICA, which can in practice be e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c9f9640d93a5272f963d8089f00ce54
https://researchportal.helsinki.fi/en/publications/8a9b7825-3f91-4f9b-ade7-9ffa3510fef2
https://researchportal.helsinki.fi/en/publications/8a9b7825-3f91-4f9b-ade7-9ffa3510fef2