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Found 3 papers, 1 papers with code
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers
This paper considers a large class of problems where we seek to recover a low rank matrix and/or sparse vector from some set of measurements.
Training Deep Neural Networks with Constrained Learning Parameters
We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics.
Low-Rank Factorization for Rank Minimization with Nonconvex Regularizers
Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis.
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