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Extracting Low-Dimensional Latent Structure from Time Series ...
Carnegie Mellon University
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Carnegie Mellon University
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由 KC Lakshmanan 著作被引用 49 次 — We demonstrate how using a gaussian process to model the evolution of each latent vari- able allows us to tractably learn these delays over a continuous domain.
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Extracting Low-Dimensional Latent Structure from Time ...
National Institutes of Health (NIH) (.gov)
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由 KC Lakshmanan 著作2015被引用 49 次 — Commonly used methods to extract these latent variables typically assume instantaneous relationships between the latent and observed variables.
Patrick Sadtler
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Extracting low-dimensional latent structure from time series in the presence of delays ... A theory of brain-computer interface learning via low-dimensional ...
Extracting latent structure from multiple interacting neural ...
ResearchGate
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2024年10月22日 — Dimensionality reduction methods attempt to identify the neural signals that are maximally correlated with sensory stimuli (Archer et al., 2014) ...
Tag: Neural Comput
University of Pittsburgh
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University of Pittsburgh
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2015年9月1日 — Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays. Lakshmanan KC, Sadtler PT, Tyler-Kabara EC, ...
Extracting a low-dimensional predictable time series
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由 Y Dong 著作2021被引用 16 次 — Extracting a low-dimensional predictable time series ; Number of pages, 26 ; Journal / Publication, Optimization and Engineering ; Volume, 23 ; Issue number, 2.
缺少字詞: Presence Delays.
Latent Diffusion for Neural Spiking Data
arXiv
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Here, we propose Latent Diffusion for Neural Spiking data (LDNS), which combines the ability of autoencoders to extract low-dimensional representations of ...
Dimensionality reduction beyond neural subspaces with ...
Nature
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由 A Pellegrino 著作2024被引用 5 次 — We develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors.
Learning Representations from Imperfect Time Series Data ...
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由 PP Liang 著作2019被引用 90 次 — 2015. Extracting low-dimensional latent structure from time series in the presence of delays. Neural. Computation, 27:1825–1856.
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Bayesian inference of structured latent spaces from neural ...
National Institutes of Health (NIH) (.gov)
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由 R Meng 著作2024 — Different methodological approaches have been developed to extract low-dimensional latent structure from single-trial population neural data. Generally speaking ...