Results 241 to 250 of about 223,248 (273)

Sparse Topical Coding with Sparse Groups

2016
Learning a latent semantic representing from a large number of short text corpora makes a profound practical significance in research and engineering. However, it is difficult to use standard topic models in microblogging environments since microblogs have short length, large amount, snarled noise and irregular modality characters, which prevent topic ...
Min Peng, Qianqian Xie, Jiajia Huang, Jiahui Zhu, Shuang Ouyang, Jimin Huang, Gang Tian   +6 more
openaire   +1 more source

Sparse coding of sensory inputs

Current Opinion in Neurobiology, 2004
Several theoretical, computational, and experimental studies suggest that neurons encode sensory information using a small number of active neurons at any given point in time. This strategy, referred to as 'sparse coding', could possibly confer several advantages. First, it allows for increased storage capacity in associative memories; second, it makes
Bruno A, Olshausen, David J, Field
openaire   +2 more sources

Sparse Coding with Anomaly Detection

Journal of Signal Processing Systems, 2013
We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model.
Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin   +3 more
openaire   +1 more source

Sparse Coding in Sparse Winner Networks

2007
This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided.
Janusz A. Starzyk, Yinyin Liu, David Vogel   +2 more
openaire   +1 more source

Deep Denoising Sparse Coding

2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), 2020
Single-cell Ribonucleic Acid sequencing (scRNA-seq) has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity.
Yijie Wang, Bo Yang
openaire   +1 more source

Sparse coding with memristor networks

Nature Nanotechnology, 2017
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a ...
Patrick M. Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, Wei D. Lu   +5 more
openaire   +2 more sources

Binary Sparse Coding

2010
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative model with binary latent variables instead of continuous-valued latents as used in classical sparse coding.
Marc Henniges, Gervasio Puertas, Jörg Bornschein, Julian Eggert, Jörg Lücke   +4 more
openaire   +1 more source

Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
exaly  

Obesity and adverse breast cancer risk and outcome: Mechanistic insights and strategies for intervention

Ca-A Cancer Journal for Clinicians, 2017
Cynthia Morata-Tarifa, Joyce M Slingerland   +1 more
exaly  

Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma

Ca-A Cancer Journal for Clinicians, 2020
Aaron J Grossberg, William L Hwang, Anirban Maitra   +2 more
exaly