Results 51 to 60 of about 20,444,531 (305)
Streaming sparse Gaussian process approximations
, 2017 Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the ...
Bui, TD, Nguyen, CV, Turner, RE
openaire +2 more sources
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Difference Frequency Gridless Sparse Array Processing
IEEE Open Journal of Signal ProcessingThis paper introduces a DOA estimation method for sources beyond the aliasing frequency. The method utilizes multiple frequencies of sources to exploit the frequency difference between them, enabling processing at a frequency below the aliasing frequency.
Yongsung Park, Peter Gerstoft
doaj +1 more source
SoftwareX, 2018 Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem.
Ervin Sejdić +2 more
doaj +1 more source
Input Dependent Sparse Gaussian Processes
, 2021 Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature. Nevertheless, GPs suffer from poor scalability as the number of training instances N increases. More precisely, they have a cubic cost with respect to $N$.
Jafrasteh, Bahram +2 more
openaire +2 more sources
Digit. Signal Process., 2017 Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time-frequency representations.
E. Sejdić, I. Orović, S. Stankovic
semanticscholar +1 more source
In-storage embedded accelerator for sparse pattern processing [PDF]
IEEE Conference on High Performance Extreme Computing, 2016 We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing ...
S. Jun, H. Nguyen, V. Gadepally, Arvind
semanticscholar +1 more source
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features
Shock and Vibration, 2016 Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable.
Wei Peng +3 more
doaj +1 more source
A unified approach to convergence rates for $\ell^1$-regularization and lacking sparsity
, 2015 In $\ell^1$-regularization, which is an important tool in signal and image processing, one usually is concerned with signals and images having a sparse representation in some suitable basis, e.g. in a wavelet basis.
Flemming, Jens +2 more
core +1 more source