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2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
Sparse coding is a widely involved technique in computer vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
Tiezheng Ge, Kaiming He, Jian Sun 0001
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Sparse coding is a widely involved technique in computer vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
Tiezheng Ge, Kaiming He, Jian Sun 0001
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SC2Net: Sparse LSTMs for Sparse Coding
Proceedings of the AAAI Conference on Artificial Intelligence, 2018The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due
Joey Tianyi Zhou +9 more
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Sparse Autoencoder for Sparse Code Multiple Access
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2021In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum.
Medini Singh +2 more
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Neurocomputing, 2014
Sparse coding has received an increasing amount of interest in recent years. It finds a basis set that captures high-level semantics in the data and learns sparse coordinates in terms of the basis set. However, most of the existing approaches fail to consider the geometrical structure of the data space.
Miao Zheng, Jiajun Bu, Chun Chen 0001
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Sparse coding has received an increasing amount of interest in recent years. It finds a basis set that captures high-level semantics in the data and learns sparse coordinates in terms of the basis set. However, most of the existing approaches fail to consider the geometrical structure of the data space.
Miao Zheng, Jiajun Bu, Chun Chen 0001
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Proceedings of the 27th ACM SIGPLAN-SIGACT symposium on Principles of programming languages, 2000
In this article, we add a third dimension to partial redundancy elimination by considering code size as a further optimization goal in addition to the more classical consideration of computation costs and register pressure. This results in a family of sparse code motion algorithms coming as modular extensions of the algorithms for busy and lazy code ...
Oliver Rüthing +2 more
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In this article, we add a third dimension to partial redundancy elimination by considering code size as a further optimization goal in addition to the more classical consideration of computation costs and register pressure. This results in a family of sparse code motion algorithms coming as modular extensions of the algorithms for busy and lazy code ...
Oliver Rüthing +2 more
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2012
We study a novel sparse coding model with discrete and symmetric prior distribution. Instead of using continuous latent variables distributed according to heavy tail distributions, the latent variables of our approach are discrete. In contrast to approaches using binary latents, we use latents with three states (-1, 0, and 1) following a symmetric and ...
Georgios Exarchakis +3 more
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We study a novel sparse coding model with discrete and symmetric prior distribution. Instead of using continuous latent variables distributed according to heavy tail distributions, the latent variables of our approach are discrete. In contrast to approaches using binary latents, we use latents with three states (-1, 0, and 1) following a symmetric and ...
Georgios Exarchakis +3 more
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2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply.
Julian Eggert, Edgar Körner
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Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply.
Julian Eggert, Edgar Körner
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Sparse Topical Coding with Sparse Groups
2016Learning 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 0002 +6 more
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Sparse Coding in Sparse Winner Networks
2007This 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 +2 more
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Sparse Coding with Anomaly Detection
Journal of Signal Processing Systems, 2013We 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 +3 more
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