sparse codingの例文

• This is why sparse coding became an alternative to grandmother cells.
• The sparse coding for the input then consists of those representative patterns.
• Sparse coding may be a general strategy of neural systems to augment memory capacity.
• They are the properties of sparse coding instead.
• Given a potentially large set of input patterns, sparse coding algorithms ( e . g.
• Sparse coding starts with the activation of moderately small sets of neurons in a small region of the brain.
• The capacity of sparse codes may be increased by simultaneous use of temporal coding, as found in the locust olfactory system.
• Theoretical work on Sparse distributed memory has suggested that sparse coding increases the capacity of associative memory by reducing overlap between representations.
• After this update \ mathbf { D } is renormalized to fit the constraints and the new sparse coding is obtained again.
• However, in 2007, Quiroga, Kreiman, Koch and Fried rescinded their initial views, trying to explain their results with sparse coding
• The idea of sparse coding is that very small numbers of neurons respond to specific features, objects or concepts in an obvious manner.
• In a recent article Quiroga, Kreiman, Koch and Fried admitted they had in fact not found grandmother cells, rather they had found sparse coding.
• This information favors sparse coding over grandmother cells, because the neurons fire only to very few stimuli, and are mostly silent with the exception of their preferred stimuli.
• The problem of finding an optimal sparse coding R with a given dictionary \ mathbf { D } is known as sparse approximation ( or sometimes just sparse coding problem ).
• The problem of finding an optimal sparse coding R with a given dictionary \ mathbf { D } is known as sparse approximation ( or sometimes just sparse coding problem ).
• Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model and to sparse coding models used in deep learning algorithms such as autoencoder.
• A major result in neural coding from Olshausen and Field is that sparse coding of natural images produces wavelet-like oriented filters that resemble the receptive fields of simple cells in the visual cortex.
• However, despite the accumulating evidence for widespread sparse coding and theoretical arguments for its importance, a demonstration that sparse coding improves the stimulus-specificity of associative memory has been lacking until recently.
• However, despite the accumulating evidence for widespread sparse coding and theoretical arguments for its importance, a demonstration that sparse coding improves the stimulus-specificity of associative memory has been lacking until recently.
• Additionally, a " hierarchical covariance model " developed by Karklin and Lewicki expands on sparse coding methods and can represent additional components of natural images such as " object location, scale, and texture ".