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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 0041
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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 0041
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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 +4 more
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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 +4 more
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Sparse coding with memristor networks
Nature Nanotechnology, 2017Sparse 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 +5 more
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The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the AAAI Conference on Artificial Intelligence, 2015
The n-gram model has been widely used to capture the local ordering of words, yet its exploding feature space often causes an estimation issue. This paper presents local context sparse coding (LCSC), a non-probabilistic topic model that effectively handles large feature spaces using sparse coding.
Seungyeon Kim 0001 +3 more
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The n-gram model has been widely used to capture the local ordering of words, yet its exploding feature space often causes an estimation issue. This paper presents local context sparse coding (LCSC), a non-probabilistic topic model that effectively handles large feature spaces using sparse coding.
Seungyeon Kim 0001 +3 more
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Sparse coding of sensory inputs
Current Opinion in Neurobiology, 2004Several 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
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Transformation invariant sparse coding
2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation invariant sparse coding (TISC) model.
Morten Mørup, Mikkel N. Schmidt
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Sparse-Graph Codes for Quantum Error Correction
IEEE Transactions on Information Theory, 2004G Mitchison, D J C Mackay
exaly
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory, 1999D J C Mackay
exaly