The impact of sparsity in low-rank recurrent neural networks.
Elizabeth HerbertSrdjan OstojicPublished in: PLoS Comput. Biol. (2022)
Keyphrases
- recurrent neural networks
- low rank
- sparsity constraints
- matrix factorization
- convex optimization
- linear combination
- missing data
- neural network
- matrix completion
- low rank matrix
- feed forward
- semi supervised
- rank minimization
- singular value decomposition
- matrix decomposition
- echo state networks
- artificial neural networks
- high dimensional
- regularized regression
- recurrent networks
- high dimensional data
- trace norm
- singular values
- high order
- minimization problems
- sparse representation
- non rigid structure from motion
- low rank matrices
- robust principal component analysis
- negative matrix factorization
- back propagation
- super resolution
- collaborative filtering
- similarity measure