Results 271 to 280 of about 864,269 (299)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +2 more sources
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +1 more source
Polyhedral Specification and Code Generation of Sparse Tensor Contraction With Co-Iteration
Transactions on Architecture and Code Optimization, 2023Michelle Strout +2 more
exaly
Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications
IEEE Transactions on Communications, 2023Vincent Poor +2 more
exaly
Sparse-Graph Codes for Quantum Error Correction
IEEE Transactions on Information Theory, 2004G Mitchison, D J C Mackay
exaly