Results 231 to 240 of about 38,175 (260)
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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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Compressed Nonnegative Sparse Coding
2010 IEEE International Conference on Data Mining, 2010Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors, has been widely applied in machine learning, signal processing and neuroscience. In this paper, we propose a dual random projection method to provide an efficient solution to Nonnegative Sparse Coding (NSC) using small memory.
Fei Wang 0001, Ping Li 0001
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2016 IEEE International Conference on Image Processing (ICIP), 2016
In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image ...
Sofia Karygianni, Pascal Frossard
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In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image ...
Sofia Karygianni, Pascal Frossard
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Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications
IEEE Transactions on Communications, 2023Jeonghun Park +2 more
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Capacity-Achieving Spatially Coupled Sparse Superposition Codes With AMP Decoding
IEEE Transactions on Information Theory, 2021Cynthia Rush +2 more
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