Results 61 to 70 of about 489,349 (320)
JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM
Sensors, 2021 Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on ...
Lu Yin, Pengcheng Ma, Zhongliang Deng
doaj +1 more source
Semantic Mapping for Lexical Sparseness Reduction in Parsing [PDF]
, 2013 Bilexical information is known to be helpful inparse disambiguation, but the benefit is limitedbecause of lexical sparseness. An approach us-ing word classes can reduce sparseness and po-tentially leads to more accurate parsing.
Suster, Simon +1 more
core +9 more sources
Evaluation of a library of loxP variants with a wide range of recombination efficiencies by Cre.
PLoS ONE, 2022 Sparse labeling of individual cells is an important approach in neuroscience and many other fields of research. Various methods have been developed to sparsely label only a small population of cells; however, there is no simple and reproducible strategy ...
Yuji Yamauchi +4 more
doaj +1 more source
Sparseness-Optimized Feature Importance [PDF]
In this paper, we propose a model-agnostic post-hoc explanation procedure devoted to computing feature attribution. The proposed method, termed Sparseness-Optimized Feature Importance (SOFI), entails solving an optimization problem related to the ...
Nápoles, Gonzalo; id_orcid, Grau, Isel
core +2 more sources
Sparse Activity and Sparse Connectivity in Supervised Learning
J. Mach. Learn. Res., 2013 Sparseness is a useful regularizer for learning in a wide range of applications, in particular in neural networks. This paper proposes a model targeted at classification tasks, where sparse activity and sparse connectivity are used to enhance classification capabilities.
Markus Thom, Günther Palm
openaire +4 more sources
Kernel heterogeneity improves sparseness of natural images representations
Neuromorphic Computing and EngineeringBoth biological and artificial neural networks inherently balance their performance with their operational cost, which characterizes their computational abilities.
Hugo J Ladret +2 more
doaj +1 more source
Frontiers in Systems Neuroscience, 2015 The dentate gyrus (DG) of mammals harbors neural stem cells that generate new dentate granule cells (DGCs) throughout life. Behavioral studies using the contextual fear discrimination paradigm have found that selectively augmenting or blocking adult ...
Kathleen Mcavoy +2 more
semanticscholar +1 more source
Characterizing the sparseness of neural codes.
, 2001 It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different ways--firstly to describe codes in which few neurons are active at any time ('population ...
B. Willmore +3 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Pediatric gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are extremely rare and clinically heterogeneous. Management has largely been extrapolated from adult practice. This European Standard Clinical Practice Guideline (ESCP), developed by the EXPeRT network in collaboration with adult NEN experts, provides (adult) evidence ...
Michaela Kuhlen +23 more
wiley +1 more source
Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing
Remote Sensing, 2017 Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing.
Risheng Huang, Xiaorun Li, Liaoying Zhao
doaj +1 more source